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/tryolabs/luminoth
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/tteepe/CenterNet-pytorch-lightning
https://github.com/tuanio/nextformer
https://github.com/tusimple/simpledet
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/uchile-robotics-forks/darknet_ros
https://github.com/ultralytics/ultralytics
https://github.com/ultralytics/yolov3
https://github.com/unsky/RetinaNet
https://github.com/unsky/focal-loss
https://github.com/usnistgov/object-detection-yolov3
https://github.com/ustc-imcl/hst-for-compressed-image-sr
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/vahidkhosh/squeezeDet
https://github.com/vahidkhosh/squeezedet-keras
https://github.com/vamshi4067/Anomaly-detection-using-computer-vision
https://github.com/vantupham/darknet
https://github.com/varunasthana92/YOLO_v3_implementation_Tensorflow2
https://github.com/vasukumar92/Autonomous-driving---Car-detection
https://github.com/ver0z/Deformable-DETR-
Deformable DETR: Deformable Transformers for End-to-End Object Detection
https://github.com/vietanhdev/centernet-bdd-data
https://github.com/vietanhdev/centernet-bdd-data-onnx-conversion
https://github.com/vietanhdev/open-adas
https://github.com/viethungluu/OCRetina
https://github.com/vietlinhtspt/NewFasterRCNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/vikrant7/pytorch-looking-fast-and-slow
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
https://github.com/vimalabs/VIMA
https://github.com/vincentzhang/faster-rcnn-fcn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/vinliao/object-detection-python
https://github.com/viskjha/Wrong-way-vehicle-detection
https://github.com/vobecant/yolov3_WannaSeeU
https://github.com/voidrank/SaccadeNet
https://github.com/vov-net/VoVNet-Detectron
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/vov-net/VoVNet-FCOS
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/vuongtrannguyenkhoi/darknet
https://github.com/vvasilo/yolov3_pytorch_ros
https://github.com/w-sugar/prtr
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.