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/youngwanLEE/vovnet-detectron2
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
https://github.com/yousefis/darknet-ocr
https://github.com/youssef-kishk/ROS-Based-Robot-Object-Detection-Recognition
https://github.com/youtang1993/mixture-model-based-object-detector
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
https://github.com/yrouphail/yolo-independent
https://github.com/yrouphail/yolov3
https://github.com/yuanxy92/darknet
https://github.com/yubaoliu/rds-slam
https://github.com/yudie433/darknet
https://github.com/yuedongli1/yolov3_mindspore
https://github.com/yukaryote/darknet
https://github.com/yuliani29/yolotraining
https://github.com/yushuz/me599_yolo
https://github.com/yuyi1005/point2rbox-mmrotate
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/ywsyws/FormationSimplon20200622dlKeras
https://github.com/yxb-nku/strip-r-cnn
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/yxlijun/Pelee.Pytorch
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/yytang2012/darknet
https://github.com/yzgrfsy/tf-fastrcnn-crop
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/zacks417/faster-rcnn-tf
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/zanmange/darknet
https://github.com/zeegeeko/YOLOv3
https://github.com/zengzhaoyang/Weak_Detection
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/zengzhaoyang/trident
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/zephyrcho/darknetV4
https://github.com/zer0sh0t/artificial_intelligence/tree/master/object_detection/you_only_look_once
https://github.com/zh320/realtime-semantic-segmentation-pytorch
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
https://github.com/zhangbo2008/swin-transformer_noted_very_detail
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/zhangchi9/Airbus_Ship_Detection
https://github.com/zhanghuiyao/yolov3_mindspore
https://github.com/zhanghuiyao/yolov5_mindspore
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/zhanghuiyao/yolov7_mindspore
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/zhangming8/libtorch-yolov3
https://github.com/zhangming8/yolox-pytorch
https://github.com/zhangxiawen/darknet-master
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.