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/Bigwode/rebar-detection-competition
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning
https://github.com/BlackAngel1111/Fast-RCNN
https://github.com/BlackAngel1111/Yolo3_Pytorch
https://github.com/BonJunKu/ScooterHelmetDetector
https://github.com/BupyeongHealer/Mask_RCNN_tf_2.x
https://github.com/Burf/SwinTransformer-Tensorflow2
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/Burf/tfdetection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/C677/Machine-learning
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/CRIGIM/darknet
https://github.com/CSSFA123/yolov4-Road
https://github.com/CUAI-CAU/Computer_vision_team_1
https://github.com/CV51GO/HarDNet_Megengine
https://github.com/CZQ0707/mindspore
https://github.com/CahideSara/deneme
https://github.com/CaiYingFeng/darknet_changed
https://github.com/Camebush/real-time-yolov4-object-detection
https://github.com/CarstenIsert/DeepBurn
https://github.com/Cedric-Perauer/Deformable_Detr_PIL
Deformable DETR: Deformable Transformers for End-to-End Object Detection
https://github.com/Chamchis/AICCTV
https://github.com/Chang-Chia-Chi/TrackNet-Badminton-Tracking-tensorflow2
https://github.com/ChelseaGH/sidewalk_prototype_AI_Hub
https://github.com/ChengLeiWei/garbage-detect-using-YOLOv3-pytorch
https://github.com/ChestnutLi/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/ChielBruin/ros_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Chris-On-PC/darknet
https://github.com/Chris10M/RFB-Text-Detection
Receptive Field Block Net for Accurate and Fast Object Detection
https://github.com/ChristianMarzahl/ObjectDetection
https://github.com/Cjiangbpcs/cjiang.github.io
https://github.com/Cjmmmmmm/BreastUltrasound_Detection
https://github.com/CoCoPIE-Pruning/CoCoPIE-ModelZoo
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
https://github.com/Code-0x00/caffe_windows
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Code-Fight/darknet_study
https://github.com/ColdNoodler/py-faster-rcnn-cuda10
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Congpeiliang/yolo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.