Research on safety helmet wearing detection algorithm for workers at water conservancy construction sites based on the HDS-DETR network

Abstract

The construction safety of workers in hydraulic construction sites that are crowded and difficult to manage is very serious. When personnel movement is frequent, the status of workers wearing safety helmets is difficult to monitor in real time. The focus of this study is the design of HDS-DETR model which is aimed to improve the safety recognition in hydraulic construction projects. Improvements were achieved by integrating the C2f-HDRAB Module to the RT-DETR model to strengthen the model's ability to detect features, the D-Attention mechanism to improve the model's ability to recognize important features, and SlimNeck architecture was implemented to improve the model's ability to efficiently fuse features. The results of the experiments reflect that the accuracy achieved was 94.1% and 89.6% of the improved model offered by the dedicated dataset in recall, and 94.9% of the mean Average Precision at IoU threshold 0.5, which is a 3.7% increase in the original model. The ablation tests demonstrate the effectiveness of the correction of modules and the proposed design is aimed at the complex nature of hydraulic construction, and provides real-time hard hat wearing monitoring. Safety management of the hydraulic engineering construction project provides support and improves the safety condition recognition in smart water conservancy construction projects.