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bibo:abstract "Manual security monitoring in female-only dormitories has limitations and is prone to negligence. To address this issue, this study designs a gender detection system as a proactive early warning system. The system utilizes deep learning-based computer vision technology to perform automatic and Real-time security monitoring.\r\nThe system was developed using a full-body recognition approach with the lightweight YOLOv8n architecture, integrated with automatic notifications via Telegram. The dataset consists of simulated CCTV images (semi top-down angles, lighting variations, and mask/helmet occlusion scenarios) as well as secondary data. The entire data was divided into 70% training data, 20% validation data, and 10% testing data using a transfer learning strategy.\r\nEvaluation results show the model achieved an mAP50 of 92.90%, precision of 90.65%, recall of 89.01%, and F1-score of 89.82%. The system proved efficient, operating stably at an average speed of 20 FPS on purely CPU-based devices without GPU acceleration. Field testing demonstrated that the full-body recognition approach remains robust in detecting subjects under occlusion. In conclusion, the YOLOv8n model provides an optimal balance between high accuracy, local computational efficiency, and operational reliability."^^xsd:string;
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