A Classification Method for Helmet Wearing State Based on Progressive Multi-Granularity Training Strategy

Abstract

In many construction sites, whether to wear the safety helmet directly affects the life safety of workers. Therefore, monitoring the wearing state of safety helmets has become an important auxiliary means of construction safety. However, most current safety helmet wearing state monitoring algorithms only distinguish workers who are wearing safety helmets from those who are not, which has high detection limitations and algorithm performance needs to be improved. In this paper, we innovatively apply fine-grained classification algorithms to classify the wearing state of safety helmets, and propose a progressive multi-granularity training strategy based safety helmet wearing state classification algorithm PMG-Helmet (Progressive Multi-granularity for Helmet, PMG-Helmet) for the six classification dataset of safety helmet wearing state. This algorithm achieves multi-granularity classification of helmet wearing state through a puzzle generator and a progressive training strategy, and introduces the MC-Loss(Mutual Channel Loss) function designed specifically for fine-grained classification tasks to improve algorithm performance. In the algorithm inference stage, this paper normalized the weights of the outputs of each stage of the PMG-Helmet algorithm, resulting in better combination accuracy. The experimental results show that the accuracy of this algorithm on the six classification dataset is 93.36%. Specifically, in order to further investigate the effectiveness of the algorithm, this study conducted separate studies on the finer subcategories of “wearing the helmet correctly” and “wearing the helmet but not fastening the chin strap” during the experimental phase, achieving an accuracy of 90.11%.