Chang, G.-C.; Zeng, B.-H.; Lin, S.-C. Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny. Appl. Sci.2024, 14, 3497.
Chang, G.-C.; Zeng, B.-H.; Lin, S.-C. Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny. Appl. Sci. 2024, 14, 3497.
Chang, G.-C.; Zeng, B.-H.; Lin, S.-C. Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny. Appl. Sci.2024, 14, 3497.
Chang, G.-C.; Zeng, B.-H.; Lin, S.-C. Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny. Appl. Sci. 2024, 14, 3497.
Abstract
This study demonstrates the efficacy of structured pruning and architectural fine-tuning on the YOLOv7-tiny model for eye state detection, emphasizing optimization for real-time applications. Structured pruning significantly reduced the model's complexity and storage size while maintaining high detection accuracy, as evidenced by stable precision, recall, and mAP@.5 metrics across iterations. Further fine-tuning adjusted the model's width and depth, optimizing efficiency and processing speed without compromising performance. These optimizations yielded YOLOv7-tiny variants that are both computationally efficient and accurate, suitable for resource-constrained environments. The findings highlight the critical role of model optimization in deploying effective neural networks for specific detection tasks in real-time scenarios.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
