Attention-Guided Underground Coal Mine Pedestrian Re-Identification Network

Pedestrian re-identification algorithms are crucial in personnel localization tasks in underground coal mines. The high similarity in attire among personnel in this environment renders general pedestrian re-identification algorithms unsuitable for personnel identification tasks in underground coal mines. This paper introduces an attention-guided feature fusion network to address the issue of poor identification accuracy arising from high similarity among personnel in coal mine scenarios. Initially, a ResNet network is utilized to extract detailed information about the target personnel. Subsequently, an attention-induced cross-level fusion module establishes a new feature fusion branch, enhancing cross-level learning and the representation of inter-class comparable subjects. Finally, contrastive global features are used to generate a powerful feature representation, reducing the difficulty of distinguishing between similar personnel. Experiments conducted on the proposed method in-house MineData dataset and the public Market-1501 dataset show that the proposed method outperforms current advanced methods, achieving mAP scores of 88.32 and 65.63, respectively.