Triplet Attention Mechanism-Based Framework for Tower Hard Hat Detection
Ming Wu, Guangxian Xu, Fei Ma · 2025
Given the poor performance of traditional approaches in identifying small hard hat objects against intricate backgrounds and over long distances, this work proposes a refined TBI-DETR algorithm. The algorithm enhances the model's recognition capability for hard hats by integrating Triplet Attention Mechanisms within the network structure and utilizing a multi-branch architecture to capture cross-dimensional interactive features of the input data. Additionally, a Bi_FPN is constructed to optimize the feature extraction process, promoting effective integration of feature information. Furthermore, a novel Inner-GIoU loss function is developed to refine training efficiency, leading to faster convergence of the model. Evaluation on the SHWD dataset reveals that the proposed approach exceeds state-of-the-art methods by at least 2% in accuracy and 1.4% in [email protected]. Its superior detection efficacy offers robust technical solutions for industrial safety management.