MD-YOLO: research on the innovation and performance optimization of vehicle detection technology
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Summary
An enhanced MD-YOLO (Multi-scale and Dynamic YOLO) model is proposed, specifically designed to address the high-performance computing challenges associated with large-scale, city-level real-time video stream analysis in intelligent transportation systems.
- Type
- article
- Published
- 2026-01-01
- Cited by
- 1
- References
- 55
- OpenAlex
- https://openalex.org/W7124928457
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:284880876
Keywords
Modular design, Minimum bounding box, Adaptability, Object detection, Precision and recall
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Are we ready for autonomous driving? The KITTI vision benchmark suite
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- YOLOv3: An Incremental Improvement
- CBAM: Convolutional Block Attention Module
- DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
- GhostNet: More Features From Cheap Operations
- End-to-End Object Detection with Transformers
- Focal and Efficient IOU Loss for Accurate Bounding Box Regression
- ultralytics/yolov5: v3.0
- Radiation, Velocity and Thermal Slips Effect Toward MHD Boundary Layer Flow Through Heat and Mass Transport of Williamson Nanofluid with Porous Medium
- SIoU Loss: More Powerful Learning for Bounding Box Regression
- Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism
- MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression
- Inner-IoU: More Effective Intersection over Union Loss with Auxiliary Bounding Box
- Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism
- Intelligent Transportation Systems for Sustainable Smart Cities
- Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation
- YOLOv10: Real-Time End-to-End Object Detection
- YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems
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