Revisión de Problemas en la Detección de Objetos en Imágenes y Videos Digitales
DOI:
https://doi.org/10.32870/recibe.v12i1.274Palabras clave:
Detección de objetos, Problemas en la detección de objetos, Aprendizaje profundoResumen
En las últimas décadas, la detección de objetos ha sido una tarea muy importante en el área de visión por computadora, ya que la detección de objetos localiza y clasifica uno o más objetos en una imagen o videos. En este artículo, se presenta una revisión de artículos y se describen técnicas clásicas y de aprendizaje profundas utilizadas para la detección de objetos. Además, se realiza una revisión de trabajos recientes sobre la detección de objetos, enfocándose en cómo se solucionan algunos de sus problemas más relevantes. Los problemas que se abarcan son: oclusión, confusión, información contextual, cambios en la iluminación, objetos pequeños y cambios de escala, variación entre la misma clase y diferentes clases, y deformación y cambios de pose. Se espera que este artículo sirva para que los interesados en el área conozcan ideas y enfoques para resolver problemas existentes en la detección de objetos incluyendo los últimos avances.Citas
Abbas S., et al. (2022). A survey of modern deep learning based object detection models. Digital Signal Processing, 103514.
Adouani A., et al. (2019). Comparision of Haar-like, HOG and LBP approaches for face detection in video sequences. 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD) (págs. 266-271). Estambul, Turquía: IEEE.
Adreopoulos A. y Tsotsos J. (2013). A Computational Learning Theory of Active Object Recognition Under Uncertainty. International Journal of Computer Vision, 95-143.
Afif M., et al. (2020). An Evaluation of RetinaNet on Indoor Object Detection for Blind and Visually Impaired Persons Assistance Navigation. Neural Processing Letters, 2265-2279.
Ajmera F., et al. (2021). Survey on Object Detection in Aerial Imagery. IEEE Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (págs. 1050-1055). Tirunelveli, India: IEEE.
Ansari S. (2019). A Review on SIFT and SURF for Underwater Image Feature Detection and Matching. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (págs. 1-4). IEEE.
Arfi A., et al. (2020). Real Time Human Face Detection and Recognition Based on Haar Features. 2020 IEEE Region 10 Symposium (TENSYMP) (págs. 517-521). IEEE.
Arnold E., et al. (2019). A Survey on 3D Object Detection Methods for Autonomous Driving Applications. IEEE Transactions on Intelligent Transportation Systems, 3782-3795.
Arulprakash E. y Aruldoss M. (2021). A study on generic object detection with emphasis on future research. Journal of King Saud University-Computer and Information Sciences.
Arunmozhi A. y Park J. (2018). Comparision of HOG, LBP and Haar-Like Features for on-Road Vehicle Detection. 2018 IEEE International Conference on Electro/Information Technology (EIT) (págs. 362-367). IEEE.
Aziz L., et al. (2020). Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review. IEEE Access, 170461-170495.
Badeka E., et al. (2020). Harvest Crate Detection for Grapes Harvesting Robot Based on YOLOv3 Model. 2020 Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), (págs. 1-5).
Bastanfard A., et al. (2019). Improving Tracking Soccer Players in Shaded Playfield Video. 2019 5th Iranina Conference on Signal Processing and Intelligent Systems (ICSPIS) (págs. 1-8). IEEE.
Bay H., et al. (2006). SURF: Speeded Up Robust Features. European Conference on Computer Vision (ECCV) (págs. 404-417). Springer.
Biswas R., et al. (2021). A new perceptual hashing method for verification and identity classification of occluded faces. Image and Vision Computing, 104186.
Biswas S., et al. (2021). Beyond document object detection: instance-level segmentation of complex layouts. International Journal on Document Analysis and Recognition, 269-281.
Bochkovskiy A., et al. (2020). YoloV4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
Borji A., et al. (2019). Salient object detection: A survey. Computational Visual Media, 117-150.
Bosquet B., et al. (2020). STDnet: Exploiting high resolution feature maps for small object detection. Engineering Applications of Artificial Intelligence, 103615.
Cao Y., et al. (2021). Few-Shot Object Detection Via Assosciation and Discrimination. Advances in Neural Information Processing.
Cen F., et al. (2021). Deep feature augmentation for occluded image classification. Pattern Recognition, 107737.
Chapel M. y Bouwmans T. (2018). New trends on moving object detection in video images captured by a moving camera: A survey. Computer Science Review, 157-177.
Chapel M. y Bouwmans T. (2020). Moving objects detection with a moving camera: A comprehensive review. Computer Science Review, 100310.
Chen G., et al. (2022). Camouflaged Object Detection via Context-aware Cross-level Fusion. IEEE Transactions on Circuit Systems and Systems for Video Technology.
Chen T., et al. (2022). Boundary-guided network for camouflaged object detection. Knowledge-Based Systems, 108901.
Chen Z., et al. (2020). Underwater salient object detection by combining 2D and 3D visual. Neurocomputing, 239-259.
Cheng G., et al. (2022). Towards Large-Scale Small Object Detection: Survey and Benchmarks. arXiv preprint arXiv:2207.13096.
Chilukuri D., et al. (2022). A robust object detection system with occlusion handling for mobile devices. Computational Intelligence.
Cygert S. y Czyzewski A. (2020). Towards Robust Pedestrian Detection With Data Augmentation. IEEE Access, 126674-126683.
Dai J., et al. (2016). R-fcn: Object detection via region-based fully convolutional networks. Advances in neural information processing systems, 379-387.
Dai J., et al. (2017). Deformable convolutional networks. Proceedings of the IEEEE International Conference on Computer Vision, (págs. 764-773).
Dai K., et al. (2019). Visual Tracking via adaptive spatially-regularized correlation filters. Proceedings of the IEEE conference on computer vision and pattern recognition (págs. 4670-4679). IEEE.
Dai X., et al. (2021). TIRNEt: Object detection in thermal infrared images for autonomous driving. Applied intelligence, 1244-1261.
Dalal N. y Triggs B. (2005). Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recogntion (CVPR'05) (págs. 886-603). IEEE.
Das A., et al. (2022). Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation. arXiv preprint arXiv:2206.07510.
Dash P. y Sigappi A. (2018). Detection and Classification of Retinal Diseases in Spectral Domain Optical Coherence Tomography Images based on SURF descriptors. 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA) (págs. 1-6). IEEE.
Deng J., et al. (2020). A review on reseatch on object detection based on deep learning. Journal of Physics: Conference Series, 12028.
Diwan T., et al. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 9243-9275.
Dong J., et al. (2020). Occlusion-Aware GAN for Face De-Occlusion in the Wild. 2020 IEEE International Conference on Multimedia and Expo (ICME) (págs. 1-6). IEEE.
Dong W., et al. (2021). Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion. IEEE Transactions on Image Processing, 2193-2206.
Du S. y Wang S. (2022). An Overview of Correlation-Filter-Based Object Tracking. IEEE Transactions on Computational Social Systems, 18-31.
Espinosa J., et al. (2020). Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review. IEEE Transactions on Intelligent Transportation Systems, 6115-6130.
Fan M., et al. (2021). Infrared small target detection based on region proposal and CNN classifier. Signal, Image and Video Processing, 1927-1936.
Fang F., et al. (2019). Combining Faster R-CNN and Model-Driven Clustering for Elongated Object Detection. IEEE Transactions on Image Processing, 2052-2065.
Feng Y., et al. (2021). Detect Faces Efficiently: A Survey and Evaluations. Transactions on Biometrics, Behavior, and Identity Source.
Flores M., et al. (2019). Pedestrian Detection Under Partial Occlusion by using Logic Inference, HOG and SVM. IEEE Latin America Transactions, 1552-1559.
Geetha S., et al. (2021). Machine Vision Based Fire Detection Techniques: A Survey. Fire Technology, 591-623.
Girshick R. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV) (págs. 1440-1448). IEEE.
Girshick R., et al. (2014). Rich feature hierarchies for accurate object detection and semantics segmentation. Proceedings of the IEEE conference on computer vision and pattern recogntion (págs. 580-587). IEEE.
Gkixioari G., et al. (2019). Mesh r-cnn. Proceedings of the IEEE/CVF International Conference on Computer Vision (págs. 9785-9795). IEEE.
Gong Y., et al. (2020). Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 34-44.
Guo J., et al. (2020). Object detector with enriched global context information. Multimedia Tools and Applications, 29551-29571.
Guo T., et al. (2020). Detection of Ice Hockey Players and Teams via a Two-Phase Cascaded CNN Model. IEEE Access, 195062-195073.
Gupta S., et al. (s.f.). Improved object recognition results using SIFT and ORB feature detector. Multimedia.
Gurbina M., et al. (2019). Tumor Detection and Classification of MRI Brain Image using Different Wavelet Transforms and Support Vector Machines. 2019 42nd International Conference on Telecommunications and Signal Processing (TSP) (págs. 505-508). IEEE.
Hajizadeh M., et al. (2023). MobileDenseNet: A new approach to object detection on mobile devices. Expert Systems with Applications.
Han B., et al. (2020). Small-Scale Pedestrian Detection Based on Deep Neural Network. IEEE Transactions on Intelligent Transportation Systems, 3046-3055.
Han J., et al. (2018). Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey. IEEE Signal Processing Magazine.
Han L., et al. (2021). Context and Structure Mining Network for Video Object Detection. International Journal of Computer Vision, 2927-2946.
He K., et al. (2015). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recogntion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1904-1916.
He K., et al. (2017). Mask r-cnn. Proceedings of the IEEE international conference on computer vision (págs. 2961-2969). IEEE.
Heo J., et al. (2022). Occlusion-aware spatial attention transformer for occluded object recognition. Pattern Recognition Letters, 70-76.
Huang G., et al. (2019). Ship detection based on squeeze excitation skip-connection path networks for optical remote sensing images. Neurocomputing, 215-223.
Huang G., et al. (2023). A Survey of Self-Supervised and Few-Shot Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4071-4089.
Huang X., et al. (2021). A bioinspired retianl neural network for accurately extracting small-target motion information in clutter backgrounds. Image and Vision Computing, 104266.
Jarunakarint V., et al. (2020). Survey and Experimental Comparision of Machine Learning Models for Motorcycle Detection. 2020 5th International Conference on Information Technology (InCIT) (págs. 320-325). IEEE.
Ji G., et al. (2022). Fast Camouglaged Objec Detection via Edge-based Reversible Re-calibration Network. 108414.
Ji S., et al. (2023). An improved algorithm for small object detection based on YOLO v4 and multi-scale contextual information. Computers and Electrical Engineering, 108490.
Jiao L., et al. (2019). A Survey of Deep Learning-Based Object Detection. IEEE Access, 128837-128868.
Jocher G. (22 de 4 de 2020). YOLOv5 by Ultralytics. Obtenido de https://github.com/ultralytics/yolov5
Jocher G., et al. (22 de 4 de 2023). YOLO by Ultralytics. Obtenido de Available: https://github.com/ultralytics/ultralytics
Kaur M. y Min C. (2018). Automatic Crop Furrow Detection for Precision Agriculture. 2018 IEEE 61st International Mildwest Symposium on Circuits and Systems (MWSCAS) (págs. 520-523). IEEE.
Kaur P., et al. (2020). A survey on brain tumor detection techniques for MR images. Multimedia Tools and Applications, 21771-21814.
Kaushal M., et al. (2018). Soft computing based object detection and tracking approaches: State-of-the-Art survey. Applied Soft Computing, 423-464.
Kellenberger B., et al. (2019). Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery Using Deep CNNs and Active Learning. IEEE Transactions on Geoscience and Remote Sensing, 9524-9533.
Kisantal M., et al. (2019). Augmentation for small object detection. arXiv preprint.
Köhler M., et al. (2023). Few-Shot Object Detection: A Comprehensive Survey. IEEE Transactions on Neural Networks and Learning Systems, 1-21.
Kreim A., et al. (s.f.). Using syntethic data for person tracking under adverse weather conditions. Image and Vision Computing, 104187.
Kumar N. (2018). Thresholding in salient object detection: a survey. Multimedia Tools and Multimedia, 19139-19170.
Kumar S., et al. (2021). An improved scheme for multifeature-based foreground detection using challenging conditions. Digital Signal Processing, 103030.
Lang W., et al. (2021). A survey of 3D object detection. Multimedia Tools and Applications, 29617-29641.
Lee G., et al. (2021). Self-Supervised Feature Enhancement Networks for Small Object Detection in Noisy Images. IEEE Signal Processing Letters, 1026-1030.
Leng J., et al. (2021). Realize your surroundings: Expliting context information for small object detection. Neurocomputing, 287-299.
Li B., et al. (2021). Beyond Max-Margin Class Margin Equilibrium for Few-Shot Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (págs. 7363-7372).
Li C. et. al. (2022). YOLOV6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv preprint arXiv:2209.02976.
Li C., et al. (2019). Face Detection Based on YOLOv3. Recent Trends in Intelligent Computing, Communication and Devices, 277-284.
Li F., et al. (2022). Faster R-CNN with Generative Adversarial Occlusion Network for Object Detection. 2022 14th International Conference on Machine Learning and Computing (ICMLC), (págs. 536-541).
Li G., et al. (2020). Faster R-CNN Deep learning Model for Pedestrian Detection from Drone Images. SN Computer Science.
Li J., et al. (2019). Multi-scale HOG feature used in object detection. Tenth International Conference on Graphics and Image Processing (ICGIP).
Li K., et al. (2020). Object detection in optical remote sensing images: A survey and a new benchmarck. ISPRS Journal of Photogrammetry and Remote Sensing, 296-307.
Li L., et al. (2021). Scale-Insensistive Object Detection via Attention Feature Pyramid Transformer Network. Neural Processing Letters.
Li N., et al. (2020). Detection of Animal Behind Cages Using Convolutional Neural Network. 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (págs. 242-245). IEEE.
Li Z., et al. (2017). Light-head r-cnn: In defense of two-stage object detector. arXiv preprint arXiv:1711.07264.
Li Z., et al. (2021). A survey of 3D object detection algorithms for intelligent vehicles development. Artifical Life and Robotics.
Liang D., et al. (2020). Lane Detection: A Survey with New Results. Journal of Computer Science and Technology, 493-505.
Liang X., et al. (2020). Small Object Detection in Unmanned Aerial Vehicle Images Using Feature Fusion and Scaling-Based Single Shot Detector With Spatial Context Analysis. IEEE Transactions on Circuits and Systems for Video Technology, 1758-1770.
Lin T., et al. (2017). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recogntion (CVPR) (págs. 2117-2125). IEEE.
Lin T., Goyal P., et al. (2017). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (págs. 2980-2988). IEEE.
Liu C., et al. (2022). Double Mask R-CNN for Pedestrian Detection in a Crowd. Mobile Information Systems.
Liu L., et al. (2019). Deep Learning for Generic Object Detection: A Survey. International Jouney of Computer Vision, 261-3128.
Liu S., Liu D., et al. (2021). Effective template upadte mechanism in visual tracking with background clutter. Neurocomputing, 616-625.
Liu S., Liu D., Seivastava G., et al. (2021). Overview of correlation filter based algorithms in object tracking. Complex and Intelligent Systems, 1895-1917.
Liu T., et al. (2020). Coupled Network for Robust Pedestrian Detection With Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling. IEEE Transactions on Image Processing, 754-766.
Liu Y., Duanmu M., et al. (2021). Exploring multi-scale deformable context and channel-wise attention for salient object detection. Neurocomputing, 92-103.
Liu Y., Peng J., et al. (2021). TSingNet: Scale-aware and context-rich feature learning for traffic sign detection and recogntion in the wild. Neurocomputing, 10-22.
Liu Y., Sun P., et al. (2021). A survey and performance evaluation of deep learning methods for small object detection. Expert Systems wirh Applications, 114602.
Lowe D. (1999). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision (págs. 1150-1157). IEEE.
Lu W., et al. (2016). SSD: Single hot MultiboBox Detector. European Conference on Computer Vision (ECCV), (págs. 21-37).
Luo H., et al. (2019). Contextual-YOLOv3: Implement Better Small Object Detection Based Deep Learning. 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), (págs. 134-142).
Luo H., et al. (2021). Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement. 21839-21851.
Meda K., et al. (2021). Artificial intelligence research within reach: an object detection model to identify rickets on pediatric wrist radiographs. Pediatric Radiology, 782-791.
Minaee S., et al. (2021). Going Deeper Into Face Detection: A Survey. arXiv preprint.
MIttal P., et al. (2020). Deep learning-based object detection in low-altitude UAV datasets: A survey. Image and Vision Computing, 204046.
Mo Y., et al. (2019). Highlight-assisted nighttime vehicle detection using a multi-level fusion network and label hierarchy. Neurocomputing, 13-23.
Mohammed S., et al. (2021). A Review on Object Detection Algorithms for Ship Detection. IEEE 7th International Conference on Advanced Computing and Communication Systems (ICACCS) (págs. 1-5). IEEE.
Mohanty S., et al. (2019). A New Approach for Movign Object Detection under Varying Illumination Enviroments. 2019 International Conference on Information Technology (ICIT), (págs. 420-424).
Moncef S. y Othman A. (2021). Efficient Techniques For Human Face Occlusions Detection and Extraction. 2021 International Conference of Women in Daa Science at Taif University (WiDSTaif), (págs. 1-5).
Mondal A. (2021). Camouflage design, assessment and breaking techniques: a survey. Multimedia Systems.
Moniruzzaman M., et al. (2017). Deep Learning on Underwater Marine Object Detection: A survey. Advanced Concepts for Intelligent Vision Systems, 150-160.
Mordan T., et al. (2019). End-to-end Learning of Latent Deformable Part-Based Representations for Object Detection. International Journal of Computer Vision, 1659-1679.
Muhammad K., et al. (2018). Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing, 30-42.
Naji S., et al. (2019). A survey on skin detection in colored images. Artificial Intelligence Review, 1041-1087.
Nguyen N., et al. (2019). A Novel Hardware Architecture for Human Detection using HOG-SVM Co-Optimization. 2019 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) (págs. 33-36). IEEE.
Ning C., et al. (2021). Survey of pedestrian detection with occlusions. Compley & Intelligent Systems, 577-587.
Nyein M. y Tint T. (2021). A Review on Advanced Detection Methods in Vehicle Traffic Scenes. IEEE 6th International Conference on Inventive Computation Technologies (ICICT) (págs. 642-649). IEEE.
Ojala T., et al. (1994). Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. Proceedings of the 12th IAPR International Conference on Pattern Recognition (págs. 582-585). IEEE.
Olarewaju M. (2021). Tomato detection based on modified YOLOv3 framework.
Pandiya M., et al. (2020). Analysis of Deep Learning Architectures for Object Detection - A critical Review. IEEE-HYDCON (págs. 1-6). IEEE.
Pathak A., et al. (2018). Deep Learning Approaches for Detecting Objects from Images: A Review. Progress in Computing, Analytics and Networking, Advances in Intelligent Sustems and Computing.
Pathare S., et al. (2020). Detection of Fractures in Long Bones for Trauma Centre Patients using Hough Transform. 2020 International Conference on Communication and Signal Processing (ICCSP) (págs. 88-91). IEEE.
R., M. (1986). Estados Unidos Patente nº 4567610.
Radhika S., et al. (2018). Determination of Degree of Damage on Building Roofs Due to Wind Disaster from Close Range Remote Sensing Images Using Texture Wavelet Analysis. IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium (págs. 3366-3369). IEEE.
Rahmatulloh A., et al. (2021). Face Mask Detection Using Haar Cascade Classifier Algorithm based on Internet of Things with Telegram Bot Notification. 2021 International Conference Advancement in Daa Science, E-learning and Information Systems (ICADEIS), (págs. 1-5).
Redmon J. y Farhadr A. (2017). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pág. 72637271). IEEE.
Redmon J. y Farhadr A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv: 1804.02767.
Redmon J., et al. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recogntion (CVPR) (págs. 779-788). IEEE.
Ren S., et al. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1137-1149.
Rodríguez A., et al. (2020). Reconocimiento de la denominación de billetes a través de una aplicación móvil con reconocimiento de imagen. ReCIBE. Reviste electrónica de Computación, Informática, Biomédica y Electrónica, 1-16.
Rublee E., et al. (2011). ORB: An efficient alternative to SIFT or SURF. 2011 International Conference on Computer Vision (ICCV) (págs. 2564-2571). IEEE.
Saez A., et al. (2019). Statistical Detection of Colors in Dermoscopic Images With a Texton-Based Estimation of Probabilities. Journal of Biomedical and Health Informatics, 560-569.
Said N., et al. (2019). Natural disasters detection in social media and satellite imagery: a survey. Multimedia Tools and Applications, 31267-21302.
Seemanthini K. y Manjunath S. (2018). Human Detection and Tracking using HOG for Action Recognition. Procedia Computer Science, 1317-1326.
Sermanet P., et al. (2013). Overfeat: Integrated recogntion, localization and detection using convolutional networks. arXiv preprint arXiv: 1312.6229.
She X., et al. (2021). ScieNet: Deep learning with spike-asisted contextual information extraction. Pattern Recognition, 108002.
Shetty A., et al. (2021). A Review: Object Detection Models. IEEE 6th International Conference for Convergence in Technology (I2CT) (págs. 1-8). IEEE.
Shi P., et al. (2023). Object Detection Based on Swin Deformable Transformer-BiPAFPN-YOLOX. Computational Intelligence and Neuroscience.
Shih H. y Chen J. (2021). Multiskin Color Segmentation Through Morphological Model Refinement. IEEE Transactions on Emerging Topics in Computational Intelligence, 225-235.
Shin D., et al. (2020). Dynamic MLML-tree based adaptive object detection using heterogeneous data distribution. Multimedia Tools and Applications, 6689-6708.
Shou X., et al. (2019). Automated Visual Inspection of Glass Bottle Bottom With Saliency Detection and Template Matching. IEEE Transactions on Instrumentation and Measurements, 4253-4267.
Singh A., et al. (2020). Animal Detection in Man-made Enviroments. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) (págs. 1427-1438). IEEE.
Siris A., et al. (2021). Scene Context-Aware Salient Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (págs. 4156-4166). IEEE.
Skoviera R., et al. (2018). Object recognition in clutter color images using Hierarchical Temporal Memory combined with salient-region detection. Neurocomputing, 172-183.
Song C., et al. (2020). Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases. 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) (págs. 187-190). IEEE.
Song X., et al. (2020). Progressive Refinement Network for Occluded Pedestrian Detection. European Conference on Computer Vision (ECCV), (págs. 32-48).
Song X., et al. (2022). PRNet++: Learning towards generalized occluded pedestrian detection via progressive refinement network. Neurocomputing, 98-115.
Sun B., et al. (2021). FSCE: Few-Shot Object Detection via Constrastive Proposal Encoding. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (págs. 7352-7362). IEEE.
Sun N., et al. (2006). Gender Classification Based on Boosting Local Binary Pattern. International Symposium on Neural Networks, (págs. 194-201).
Suruliandi A., et al. (2012). Local binary pattern and its derivatives for face recognition. IET Computer Vision, 480-488.
Terven J. y Cordova D. (2023). A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond. arXiv preprint arXiv:2304.004501.
Tong K., et al. (2020). Recent advances in small object detection based on deep learning: A review. Image and Vision Computing, 103910.
Ullah I., et al. (2020). A brief survey of visual saliency detection. Multimedia Tools and Applications, 34605-34645.
Vashisht M. y Kumar B. (2020). A Survey Paper on Object Detection Methods in Image Processing. International Conference on Computer Science, Engineering and Applications (ICCSEA) (págs. 1-4). IEEE.
Viola P. y Jones M. (2004). Robust Real-Time Face Detection. International Jounral of Computer Vision, 137-154.
Wang C., et al. (2022). Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696.
Wang H., et al. (s.f.). Pose-guided part matching network via shriking and reweighting for occluded person re-identification. Image and Vision Computing.
Wang K., et al. (2021). D2C-Net: A Dual-branch, Dual-guidance and Cross-refine Network for Camouflaged Object Detection. IEEE Transactions on Industrial Electronics.
Wang K., Peng X., Yang J., Meng D. y Qiao Y. (2020). Region Attention Networks for Pose and Occlusion Robust Facia Expression Recognition. IEEE Transactions on Image Processing, 29, 4057-4069.
Wang L., et al. (2021). Giant Panda Identification. IEEE Transactions on Image Processing, 2837-2849.
Wang Q., et al. (2020). Overview of deep-learning based methods for salient object detection in videos. Pattern Recognition, 107340.
Wang S., et al. (2020). Visual Context Aware Ship Detector for High-Resolution SAR Imagery. 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (págs. 1778-1781). IEEE.
Wang W., et al. (2021). Salient Object Detection in the Deep Learning Era: An In-depth Survey. Transactions on Pattern Analysis and Machine Intelligence.
Wang Y., et al. (2020). A Camouflaged Object Detection Model Based on Deep Learning. 2020 IEEE International Conference on Artificial Intelligence and information Systems (ICAIIS) (págs. 150-153). IEEE.
Wang Y., et al. (2022). Detecting Occluded and Dense Trees in Urban Terrestrial Views with High-quality Tree Detecton Dataset. IEEE Transactions on Geoscience and Remote Sensing.
Wank K., et al. (2020). Region Attention Networks for Pose and Occlusion Robust Facial Expression Recogntion. IEEE Transactions on Image Processing, 4057-4069.
Wei W., et al. (2019). Occluded Pedestrian Detection Based on Depth Vision Significance in Biomimetic Binocular. IEEE Sensors Journal, 11469-11474.
Wei X., et al. (2020). Pedestrian detection in underground mines via parallel feature transfer network. Pattern Recognition, 107195.
Wu C. y Ding J. (2018). Occluded face recognition using low-rank regression with generalized gradient direction. Pattern Recognition, 256-268.
Wu D., et al. (2020). Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural enviroemnt. Computers and Electronics in Agriculture, 105742.
Wu T. (2020). A Supernova Detection Implementation based on Faster R-CNN. 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) (págs. 390-393). IEEE.
Wu Y., et al. (2020). Real-time traffic sign detection and classification towards real traffic scene. Multimedia Tools and Applications, 18201-18219.
Xiang L., et al. (2023). DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection. arXiv preprint arXiv:2301.05565.
Xiao B., et al. (2021). A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement. Automation in Construction, 103721.
Xiao Y., et al. (2020). A review of object detection based on deep learning. Multimedia Tools and Applications, 23729-23791.
Xie H., et al. (2019). Context-aware pedestrian detection especially for small-sized instances with Deconvolution Integrated Faster RCNN (DIF R-CNN). Applied Intelligence, 1200-1211.
Xie J., et al. (2020). Mask-Guided Attention Network and Occlusion-Sensitive Hard Example Mining for Occluded Pedestrian Detection. IEEE Transactions on Image Processing, 3872-3884.
Xie S., et al. (2020). Diverse receptive field network with contect aggregation for fast object detection. Journal of Visual Communication and Image Representation, 102770.
Xu S., Gu J., et al. (2023). DKTNet: Dual-Key Transformer Network for small object detection. Neurocomputing, 29-41.
Xu S., Zhang M., et al. (2023). A systematic review and analysis of deep learning-based underwater object detection. Neurocomputing, 204-232.
Xu X., et al. (2021). Boundary guidance network for camouflage object detection. Image and Vision Computing, 104283.
Xu Y., et al. (2018). Rapid Pedestrian Detection Based on Deep Omega-Shape Features with Partial Occlusion Handing. Neural Processing Letters, 923-937.
Yan Z., et al. (2021). Detection-Oriented Backbone Trained from Near Scratch and Local Feature Refinement for Small Object Detection. Neural Processing Letters, 1921-1943.
Yilmaz B. y Karsligil M. (2020). Detection Of Airplane And Airplane Parts From Security Camera Images with Deep Learning. 2020 28th Signal Processing and Communications Applications Conference (SIU) (págs. 1-4). IEEE.
Yuan J., et al. (2020). Gated CNN: Integrating multi-scale feature layers for object detection. Pattern Recognition, 107131.
Yuan Y., Xiong Z. y Wang W. (2019). VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection. IEEE Transactions on Image Processing, 3423-2434.
Yudin D., et al. (2019). Detection of Big Animals on Images with Road Scenes using Deep Learning. 2019 International Conference on Artificial Intelligence Applications and Innovations (págs. 100-103). IEEE.
Zardoua Y., et al. (2021). A Horizon Detection Algorithm for Maritime Surveillance. arXiv preprint.
Zhang B., et al. (2010). Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor. IEEE Transactions on Image Processing, 533-544.
Zhang C., et al. (2022). Camouflaged object detection via Neighbor Connection and Hieratchical Information Transfer. Computer Vision and image Understanding, 103450.
Zhang G., et al. (2004). Boosting Local Binary Pattern (LBP)-Based Face Recognition. Chinese Conference on Biometric Recogntion, (págs. 179-186).
Zhang G., et al. (2019). CAD-Net: A Context-Aware Detection Network for objects in Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, 10015-10024.
Zhang H. y Hong X. (2019). Recent progress on object detection: a brief review. Multimedia Tools and Applications, 27809-27847.
Zhang H., et al. (2021). An empirical study of multi-scale object detection in high resolution UAV images. Neurocomputing, 173-182.
Zhang K., et al. (2021). Semantic Context-Aware Network for Multiscale Object Detection in Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, 1-5.
Zhang L., et al. (2020). A Multistage Refinement Network for Salient Object Detection. IEEE Transactions on image Processing, 3534-3545.
Zhang P., et al. (2020). Non-rigid object tracking via deep multi-scale spatial-temporal saliency maps. Pattern Recognition, 107130.
Zhang Q., et al. (2020). Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images. IEEE Transactions on Image Processing, 1305-1317.
Zhang S., et al. (2020). Asymmetric multi-stage CNNs for small-scale pedestrian detection. Neurocomputing, 12-26.
Zhang T., et al. (2020). Omni-supervised joint detection and pose estimation for wild animals. Pattern Recognition Letters, 84-90.
Zhang W., et al. (2021). Global context aware RCNN for object detection. Neural Computing and Applications, 11627-11639.
Zhang X., et al. (2022). PSNEt: Perspective-sensitive convolutional network for object detection. Neurocomputing, 384-395.
Zhang X., Liu Y., Huo C., Xu N., Wang L. y Pan C. (2022). PSNEt: Perspective-sensitive convolutional network for object detection. Neurocomputing, 384-395.
Zhang Y., et al. (2019). Detecting small faces in the wild based on generative adversarial network and contextual information. Pattern Recognition, 74-86.
Zhao M., et al. (2021). An occlusion-resistan circle detector using inscribed triangles. Pattern Recognition, 107588.
Zheng H., et al. (2020). Feature enhacnement for multi-scale object detection. Neural Processing Letters, 1907-1919.
Zheng Q. y Chen Y. (2021). Interactive multi-scale feature representation enhancement for small object detection. Image and Vision Computing, 104128.
Zheng W., et al. (2020). A novel approach inspired by optic nerve characteristics for few-shot occluded face recogntion. Neurocomputing, 25-41.
Zheng Y., et al. (2019). Detection of People With Camouflage Pattern Via Dense Deconvolution Network. IEEE Signal Processing Letters, 29-33.
Zhou C. y Yuan J. (2019). Multi Pattern Recognition-label learning of part detectors for occluded pedestrian detection. 99-111.
Zhou C. y Yuan J. (2020). Occlusion Pattern Discovery for Object Detection and Occlusion Reasoning. IEEE Transactions on Circuits and Systems for Video Technology, 2067-2080.
Zhou K., et al. (2020). Improving Multispectral Pedestrian Detection by Addresing Modality Imbalance Problems. European Conference on Computer Vision (ECCV), (págs. 787-803).
Zhou S., et al. (2019). Discriminative Feature Learning With Foreground Attention for Person Re-Identification. IEEE Transactions on Image Processing, 4671-4684.
Zhou S., et al. (2020). Depth occlusion perception feature analysis for person re-identification. Pattern Recognition Letters, 617-623.
Zhou T., et al. (2021). RGB-D salient object detection: A survey. Computational Visual Media, 37-69.
Zhou X. y Zhang L. (2022). SA-FPN: An effective feature pyramid network for crowded human detection. Applied Intelligence.
Zhu Y., et al. (2019). Attention CoupleNet: Fully Convolutional Attention Coupling Network for Object Detection. IEEE Transactions on Image Processing, 113-126.
Zhuge M., et al. (2022). CubeNet: X-shape connection for camouflaged object detection. Pattern Recognition, 108644.
Zou Z., et al. (2023). Object Detection in 20 Years: A Survey. Proceedings of the IEEE, 257-276.
Zuo J., et al. (2018). Person Re-Identification. 2018 IEEE International Conference on Multimedia and Expo (ICME) (págs. 1-6). IEEE.
