Summary of Dgnn-yolo: Interpretable Dynamic Graph Neural Networks with Yolo11 For Small Occluded Object Detection and Tracking, by Shahriar Soudeep et al.
DGNN-YOLO: Interpretable Dynamic Graph Neural Networks with YOLO11 for Small Occluded Object Detection and Tracking
by Shahriar Soudeep, M. F. Mridha, Md Abrar Jahin, Nilanjan Dey
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, DGNN-YOLO, addresses the challenges of detecting and tracking small, occluded objects like pedestrians, cyclists, and motorbikes in dynamic urban environments. By integrating dynamic graph neural networks (DGNNs) with YOLO11, this novel approach leverages the strengths of both models to improve real-time data updates and resource efficiency. The framework constructs and regularly updates its graph representations, capturing object interactions as edges, allowing it to adapt to rapidly changing conditions. Additionally, Grad-CAM, Grad-CAM++, and Eigen-CAM visualization techniques enhance interpretability and trust by providing insights into the model’s decision-making process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study introduces a new way to detect and track small objects in traffic surveillance systems. The proposed framework, DGNN-YOLO, is better at handling real-time data updates and small moving objects than previous methods like YOLO11. It does this by using special kinds of neural networks called graph neural networks that can adapt to changing conditions. This makes it useful for traffic surveillance systems that need to track many different types of vehicles and pedestrians. |
Keywords
» Artificial intelligence » Tracking » Yolo
