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/stevedepp/yolo4asl
https://github.com/stevenzhou2017/MaskYolo
https://github.com/stevenzhou2017/darknet_AlexeyAB
https://github.com/stigma0617/VoVNet-DeepLabV3
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/stigma0617/VoVNet.pytorch
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/stigma0617/maskrcnn-benchmark-vovnet
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/stupidZZ/pyc_repo
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/subodh-malgonde/vehicle-detection
https://github.com/sudharavali/objectDetectionYOLO
https://github.com/sujaygarlanka/basketball-shot-count
https://github.com/sujaygarlanka/basketball_shot_count
https://github.com/sulasen/race-events-recognition-1
https://github.com/sung0471/pytorch-YOLOv4
https://github.com/sunhui1234/haha
https://github.com/sunlizhuang/YOLOv1-PaddlePaddle
https://github.com/sunqiangxtcsun/faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/supernlogn/squeezeDetTL
https://github.com/sushmaakoju/yolo
https://github.com/suvasis/birdnet2cs231n
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/swaubhik/social-distance-detector
https://github.com/t-hanya/kuzushiji-recognition
https://github.com/tade0726/yolov3_ui_error
https://github.com/taeokimeng/object-detection-yolo
https://github.com/tahmid0007/DETR_FineTune
https://github.com/takooctopus/CornerNet-Lite-Tako
https://github.com/takooctopus/Yolov3-Tako
https://github.com/tallysprado/myDarknet
https://github.com/talshaharabany/DeepACM2D
https://github.com/tanimutomo/ganonymizer
https://github.com/tanjeffreyz/yolo-v1
https://github.com/tanmayj000/darknet
https://github.com/taranjotsingh01/Car-detection-using-YOLO
https://github.com/tdurand/darknet-withtiling
https://github.com/teemoeric/projet
https://github.com/tensorboy/centerpose
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.