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/RobotMobile/cv-deep-learning-paper-review
https://github.com/Rohed/ml-1
https://github.com/RunzhaoHuang/DeepSort_YOLOV5_OnScreen
https://github.com/RuoyuChen10/objectdetection-saliency-maps
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/SAIGANESH-Collab/DARKNET
https://github.com/SAIGANESH-Collab/YOLO-AQUA
https://github.com/SHU-FLYMAN/Yolov1-TensorFlow
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SUYEgit/Surgery-Robot-Detection-Segmentation
https://github.com/Saloni1Parekh609/MIDAS
https://github.com/SanketAgrawal/Catagorical-Focal-Loss
https://github.com/SarthakYadav/audax
https://github.com/Seioch/darknet_v4_deploy
https://github.com/SeventhBlue/trainDarknet-yolov3
https://github.com/SeventhBlue/trainDarknet-yolov4
https://github.com/SfTI-Robotics/ROS-label-node
https://github.com/SforAiDl/vformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/ShaharrHorn/Detection-Game-logic
https://github.com/ShaojieJiang/tldr
https://github.com/SharifElfouly/easy-model-zoo
https://github.com/Sharpiless/paddlex-driver-state-recognition
https://github.com/Sharpiless/paddlex-vehicle-detection-with-YOLOv3
https://github.com/Sharpiless/yolov3-vehicle-detection-paddle
https://github.com/Shellllllly/plateDetection
https://github.com/ShigemichiMatsuzaki/MSPL
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
https://github.com/Shraddha2013/customyolo
https://github.com/Shraddha2013/darknett
https://github.com/Shraddha2013/darknetyol
https://github.com/Shraddha2013/darknetyolo
https://github.com/Shraddha2013/darknetyolo3
https://github.com/Shraddha2013/mydarknetyolo
https://github.com/Shraddha2013/myfileyolo
https://github.com/Shulk97/JPEG_detection_INSA_ROUEN_project
https://github.com/Shulk97/JPEG_detection_Resnet_SSD
https://github.com/ShunyuYao/blitznet_instance_segment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.