Dress Code Monitoring Method in Industrial Scene Based on Improved YOLOv8n and DeepSORT

Abstract

Deep learning-based object detection has become a powerful tool in dress code monitoring. However, even state-of-the-art detection models inevitably suffer from false alarms or missed detections, especially when handling small targets such as hats and masks. To overcome these limitations, this paper proposes a novel method for dress code monitoring using an improved YOLOv8n model, the DeepSORT tracking, and a new dress code judgment criterion. We improve the YOLOv8n model through three means: (1) a new neck structure named FPN-PAN-FPN (FPF) is introduced to enhance the model’s feature fusion capability, (2) Receptive-Field Attention convolutional operation (RFAConv) is utilized to better capture the difference in information brought by different positions, and a (3) Focused Linear Attention (FLatten) mechanism is added to expand the model’s receptive field. This improved YOLOv8n model increases mAP while reducing model size. Next, DeepSORT is integrated to obtain instance information across multi-frames. Finally, we adopt a new judgment criterion to conduct real-scene dress code monitoring. The experimental results show that our method effectively identifies instances of dress violations, reduces false alarms, and improves accuracy.