A Multimodal Fusion Model for Enhanced Industrial Glove-Wearing Compliance Detection

Glove detection in manufacturing environments is challenging due to glove-background blending and limited dataset diversity. To address this, we propose a multimodal detection framework that enhances segmentation models through wrist keypoint-guided feature fusion, effectively reducing false negatives. We also introduce a unified dataset spanning five manufacturing domains to improve generalizability. Experimental results show our method achieves mAP 0.821, outperforming the baseline YOLOv11-Seg (mAP 0.792). This highlights the effectiveness of feature fusion between segmentation and keypoints for accurate and reliable glove compliance monitoring in industrial settings.