Mobile phone detection in images based on improved YOLOv8

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.