Summary of Research on Target Detection Method Of Distracted Driving Behavior Based on Improved Yolov8, by Shiquan Shen et al.
Research on target detection method of distracted driving behavior based on improved YOLOv8
by Shiquan Shen, Zhizhong Wu, Pan Zhang
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes an improved YOLOv8 detection method for detecting distracted driving behavior using deep learning technology. The existing methods are computationally intensive and redundant, limiting their practical applications. To address this issue, the authors integrate a BoTNet module, GAM attention mechanism, and EIoU loss function into the original YOLOv8 model to optimize feature extraction and multi-scale feature fusion strategies. This optimization simplifies the training and inference processes, resulting in improved detection accuracy and efficiency. The proposed method achieves an accuracy rate of 99.4% and is smaller and easier to deploy than existing methods, enabling real-time identification and classification of distracted driving behaviors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way for computers to detect when drivers are not paying attention while behind the wheel. Right now, these detection systems can be slow or too big to use in everyday life. The researchers fix this problem by combining three key ideas: BoTNet, GAM, and EIoU loss function. This combination helps the system learn better and faster, making it useful for real-life situations where quick warnings are needed. The new method is so good that it can accurately detect distracted driving behavior 99.4% of the time! |
Keywords
» Artificial intelligence » Attention » Classification » Deep learning » Feature extraction » Inference » Loss function » Optimization
