Real-Time Object Detection
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Real-Time Object Detection is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.
This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.
<span style="color:grey; opacity: 0.6">( Image credit: CenterNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/TuSimple/mx-maskrcnn
https://github.com/TusharMhaske28/Object-Detection
https://github.com/TusharMhaske28/Thermal-Object
https://github.com/TusharMhaske28/summer-project
https://github.com/UPCLJ/py-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/USTC-IMCL/HST-for-Compressed-SR
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Ugenteraan/YOLOv1
https://github.com/UtkarshKharche29/Automatic-Number-Plate-Recognition
https://github.com/V3Det/mmdetection-V3Det
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/VCasecnikovs/Yet-Another-YOLOv4-Pytorch
https://github.com/VDIGPKU/OPANAS
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/VasuTammisetti/Object-detection-using-YOLO_V3-Darknet
https://github.com/VcampSoldiers/Swin-Transformer-Tensorflow
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Veternal1226/YOLO_darknet
https://github.com/Vijayabhaskar96/Object-Detection-Algorithms
https://github.com/VinishUchiha/Object-Detection-Using-Yolo4
https://github.com/Visual-Behavior/aloception
https://github.com/Visual-Behavior/detr-tensorflow
https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet
https://github.com/Viveksbawa/SARAS-ESAD-Baseline
https://github.com/Vonski/wdi19
https://github.com/WYGNG/CenterNet-Pro
https://github.com/WYGNG/USTC_SSE_Project
https://github.com/WaelOuni/MergeTenserFlowWithOdb
https://github.com/WalterMa/gluon-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/WangFeng18/Swin-Transformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/WangMian-Maker/Flask
https://github.com/Wangxy2180/darknetKinectDetect
https://github.com/Wastoon/XinTong_CenterNet
https://github.com/Westlake-AI/openmixup
https://github.com/Windxy/YOLOv4-Based-on-PaddleDetection
https://github.com/WinstonLy/Electricity-Inspection-Based-Ascend310
https://github.com/WuJiang1996/pytorch-YOLOv4
https://github.com/WuPedin/Multi-class_Yolov5_DeepSort_Pytorch
https://github.com/XinYangDong/ttfnet-master2
Training-Time-Friendly Network for Real-Time Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.