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19 July 2024 Mobile phone detection in images based on improved YOLOv8
Rui Zhong, Lizuo Jin
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Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132X (2024) https://doi.org/10.1117/12.3035301
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China
Abstract
Banks often need to collect and store customers' personal information due to the nature of their business. While this business model provides convenience to customers, it also increases the risk of personal information leakage. The widespread use of monitoring devices has led to rapid development in intelligent supervision technology. This research focuses on the actual work scenario at bank counters, aiming to intelligently recognize tellers' behavior in using mobile phones. Considering the requirements for both model accuracy and real-time performance, we propose our model based on YOLOv8 for the mobile phone detection task. To address the accuracy issues in the original model, the paper enhances the model's learning on hard samples and introduces an attention mechanism to improve accuracy. To tackle the real-time performance issues in original model, the paper optimizes convolution structures and adopts model channel pruning to enhance inference speed. Our model achieves a great balance between accuracy and real-time performance through comparative experiments before and after improvement.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Rui Zhong and Lizuo Jin "Mobile phone detection in images based on improved YOLOv8", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132X (19 July 2024); https://doi.org/10.1117/12.3035301
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KEYWORDS
Cell phones
Detection and tracking algorithms
Object detection
Video
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