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/Eric3911/ScaledYOLOv4
https://github.com/ErikGDev/instance-segmentation
https://github.com/Everina/car-detection-yolo
https://github.com/FL77N/RetinaNet-Based-on-PPdet
https://github.com/FMsunyh/keras-yolo2
https://github.com/FarzamIHPC/YoloV3_mask
https://github.com/FateScript/CenterNet-better
https://github.com/Feynman27/pytorch-detect-rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/FlorinAndrei/TensorAim
https://github.com/FlyingAnt2018/darknet-modify
https://github.com/GAOwy123/py-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/GH3927/Mask-RCNN-applied-to-cranes
https://github.com/GXYM/Focal-loss
https://github.com/GZQ0723/YoloV4
https://github.com/GangmingZhao/GraphFPN-Graph-Feature-Pyramid-Network-for-Object-Detection
https://github.com/Gaurav14cs17/YOLOE
https://github.com/Gavino7/YOLOv3set
https://github.com/GeeGeeWP/SCML
https://github.com/George-Ogden/Mask-RCNN
https://github.com/GiaKhangLuu/YOLOv1_from_scratch
https://github.com/GianRomani/YOLOv3-Neural-Networks-project-
https://github.com/GiantPandaCV/yolov3-point
https://github.com/GibranBenitez/IPN-hand
https://github.com/GiulioFischietti/Tirocinio
https://github.com/GivralNguyen/-Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU-
Receptive Field Block Net for Accurate and Fast Object Detection
https://github.com/GivralNguyen/Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU
Receptive Field Block Net for Accurate and Fast Object Detection
https://github.com/GodofCCode/yolo3
https://github.com/Gojay001/toolkit-DeepLearning
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Guanghan/CenterNet-Gluon
https://github.com/Guanghan/ROLO
https://github.com/Guanghan/mxnet-centernet
https://github.com/Guillem96/efficient-net-tf
https://github.com/Guillem96/efficientdet-tf
https://github.com/GunjanChourasia/custom_detector_yolov3
https://github.com/GuoQuanhao/EfficientDet-Paddle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.