Attention-Augmented PointPillars for Enhanced Mining Personnel Detection

The intricate layout of underground mine tunnels and the operation of large-scale mining equipment create extensive blind zones, leading to an average of 17 personnel collision accidents per 100,000 working hours in China’s metal mines. To tackle this issue, we constructed a specialized Jinchuan Underground Mining Personnel Dataset covering intersecting tunnels and long straight tunnels, with precise bounding box annotations for personnel locations under varying illumination and dust conditions. We propose the Attention-Augmented PointPillars for Enhanced Mining Personnel Detection. Incorporating Recursive Gated Convolutions into the feature extraction network enables long-range modeling and higher-order spatial interactions. Moreover, the pyramidal design in gn Conv with channel width gradually increasing during spatial interactions enhances the model’s efficiency in processing complex spatial information. Additionally, a Channel and Spatial Attention module integrating spatial and channel attention feature fusion strengthens feature expression via multiple weighting mechanisms. Field tests in Jinchuan underground mine show optimal performance with a batch size of 8, a learning rate of 0.003, and a spatial interaction order of 5, achieving 3% higher accuracy than the original network. Furthermore, comparisons with mainstream methods on the Underground Personnel Dataset confirm our method’s state-of-the-art performance.