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/cviviers/YOLOv5-6D-Pose
https://github.com/cwbabel/faster-cnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/dafanghe/Tensorflow_SceneText_Oriented_Box_Predictor
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/damien911224/theWorldInSafety
https://github.com/dangWV/yolov3_darknet53
https://github.com/danijorgesantos/detectcars
https://github.com/darkAlert/yolov3-rt
https://github.com/darshans0200/YOLOTest
https://github.com/daveboat/pytorch_focal_loss
https://github.com/david8862/keras-YOLOv3-model-set
https://github.com/daxiapazi/faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/dayfixer/darknet-comment
https://github.com/dc17540/darknet
https://github.com/dec880126/Self-driving-with-YOLO
https://github.com/dec880126/YOLOv4-Self-driving
https://github.com/deeplearnproject/darknet
https://github.com/delldu/MaskRCNN
https://github.com/delmalih/MIAS-mammography-obj-detection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/deolipankaj/Stone_Detection_MRCNN
https://github.com/detectionteamucas/retinanet_tensorflow
https://github.com/devsoft123/fast-cnn
https://github.com/dheerajpreddy/CenterNet-without-DCN
https://github.com/dianzl/sodformer
https://github.com/dipuchak95/dip_net
https://github.com/dishen12/py03
https://github.com/djkim3/Yet-Another-EfficientDet-Pytorch
https://github.com/dnozza/profanity-obfuscation
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/doheelab/pku-CenterNet
https://github.com/dongkyuk/ConvNext-tensorflow
https://github.com/dongkyuk0419/YOLO
https://github.com/dorbodwolf/darknet
https://github.com/dragon20002/STLC_Server
https://github.com/dreamway/CenterNet-objects-as-points
https://github.com/drscotthawley/SPNet
https://github.com/dtthi/darknetAlexeyAB
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.