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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/datamllab/bed_main
공식 코드https://github.com/djordjened92/yudo
공식 코드https://github.com/dongchen06/scooterdet
공식 코드Performance Evaluation of Real-Time Object Detection for Electric Scooters
https://github.com/facebookresearch/ConvNeXt
공식 코드https://github.com/facebookresearch/detectron
공식 코드https://github.com/facebookresearch/detr
공식 코드https://github.com/fishandwasabi/yolo-ms
공식 코드YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection
https://github.com/fundamentalvision/Deformable-DETR
공식 코드Deformable DETR: Deformable Transformers for End-to-End Object Detection
https://github.com/git-disl/EVA
공식 코드Parallel Detection for Efficient Video Analytics at the Edge
https://github.com/git-disl/TOG
공식 코드TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
https://github.com/google/automl
공식 코드https://github.com/huawei-noah/Efficient-Computing
공식 코드Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
https://github.com/huawei-noah/Efficient-Computing/tree/master/Detection/Gold-YOLO
공식 코드Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
https://github.com/huiminshii/dpnet
공식 코드DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention
https://github.com/icey-zhang/SuperYOLO
공식 코드SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
https://github.com/imankgoyal/NonDeepNetworks
공식 코드https://github.com/imoonlab/yolov13
공식 코드YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
https://github.com/inakierregueab/yolo11-jde
공식 코드YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
https://github.com/justincdavis/cv2ext/tree/main/src/cv2ext/research/shift
공식 코드Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute Systems
https://github.com/lachlants/denet
공식 코드DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
https://github.com/lijfrank-open/SFCHD-SCALE
공식 코드Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method
https://github.com/lyuwenyu/RT-DETR
공식 코드https://github.com/meituan/yolov6
공식 코드YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
https://github.com/microsoft/Swin-Transformer
공식 코드Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
https://github.com/microsoft/regionclip
공식 코드https://github.com/ming71/CFC-Net
공식 코드https://github.com/mkang315/CST-YOLO
공식 코드CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
https://github.com/mkang315/bgf-yolo
공식 코드BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
https://github.com/mkang315/rcs-yolo
공식 코드RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
https://github.com/navervision/mlsd
공식 코드https://github.com/nightsnack/YOLObile
공식 코드YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
https://github.com/om-ai-lab/OmDet
공식 코드Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head
https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet
공식 코드RTMDet: An Empirical Study of Designing Real-Time Object Detectors
https://github.com/paul-shuvo/planar_pose
공식 코드https://github.com/pingyang1117/PRBNet_PyTorch
공식 코드Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.