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/Popoooo/darknet_test
https://github.com/Poseidon0711/Autonomous-driving-application---Car-detection
https://github.com/PotatoSpudowski/CactiNet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Pritam-N/ParNet
https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Qengineering/MobileNetV1_SSD_OpenCV_Caffe
https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn
https://github.com/Qengineering/PeleeNet_SSD
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/Qengineering/Rfcn_ncnn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/Qengineering/YoloV2-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV3-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-64-bit
https://github.com/Qinhj07/ATOMCode
https://github.com/RACHANAMOVVA09/CenterNet
https://github.com/Raghvender1205/ConvNeXt
https://github.com/Rahmanzia3/darknet
https://github.com/Rahmanzia3/darknet_original
https://github.com/Rahmanzia3/yolo
https://github.com/RaiyaniNirav/Mask-R-CNN-for-water-detection
https://github.com/RajArPatra/Super-OCR
https://github.com/Rakeshvd/Instance-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/Rakeshvd/Semantic-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/RangiLyu/mmdetection_test
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/RangiLyu/nanodet
https://github.com/Raychou0830/Carplate
https://github.com/RedWang1989/YOLOv4
https://github.com/RentadroneCL/Photovoltaic_Fault_Detector
https://github.com/RentadroneCL/model-definition
https://github.com/RichardMathewsII/YOLBO
https://github.com/Ritik-Sharma38/YoloV3_training_Hands
https://github.com/RituYadav92/Radar-RGB-Attentive-Multimodal-Object-Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.