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/GuoQuanhao/YOLOv4-Paddle
https://github.com/HSRW-EMRP-WS1920-Drone/object_detection_and_landing
https://github.com/HVision-NKU/Strip-R-CNN
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/HaiqalLM10/YOLO-Vehicle-Counting
https://github.com/HardLaugh/EfficientDet-bifpn
https://github.com/Hardly-Human/Yolo-Object-Detector
https://github.com/HeegonJin/yolov1
https://github.com/Hiwyl/yolov3-attentation
https://github.com/HongSic/DarknetAI
https://github.com/Hrishkesh/YoloInvoiceExtraction
https://github.com/HungryCookie/VisDrone_CenterNet
https://github.com/HzcIrving/DeepLearning_PlayGround/tree/main/Swin-Transformer
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/IDEA-Research/Grounded-Segment-Anything
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/IDEA-Research/detrex
https://github.com/IDEA-opensource/DAB-DETR
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/IDEA-opensource/DN-DETR
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/IDEACVR/MaskDINO
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/IMvision12/keras-vision-models
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/IPLI/IPLI_Server
https://github.com/IdiosyncraticDragon/relief_rcnn
Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection
https://github.com/IgorMunizS/mobileDet
https://github.com/IlyaOvodov/AngelinaReader
https://github.com/Iqbal1282/yolov3_incremental_distillation_loss
https://github.com/JC1DA/yolo_streamer
https://github.com/JCamERG/YOLOPersonal
https://github.com/JEFfersusu/RT-DSAFDet
Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an example
https://github.com/JEONGHA-SHIN/YOGO_Server_v2
https://github.com/JHKim-snu/GVCCI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/JHKim-snu/PGA
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/JIA-HONG-CHU/Swin-Transformer-add-EncNet-DaNet-DraNet-for-semantic-segmentation-on-Statelite-Dataset
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/JWMON/yolo
https://github.com/JagadishSivakumar/GlobalWheatDetection
https://github.com/JakobGroeftehauge/BachelorProject
https://github.com/JakobGroeftehauge/Brick-Pose-Estimation
https://github.com/JaryHuang/awesome_SSD_FPN_GIoU
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.