Research on Real-time Identification and Monitoring of Miners' Safety Helmets Based on Deep Convolutional Neural Networks

With the advancement of industrialization, the safety protection of mines has entered the high-quality development stage. In line with human-centered thinking, we propose using technological measures to meet miners' safety assurance needs better. Based on the dynamic evolution of the deep convolutional neural networks, the theoretical analysis framework of safety helmet detection is constructed according to the end-to-end internal logic. Therefore, we can describe the data collection mechanism, including video capture and image preprocessing, and the safety detection mechanism generated by the safety helmet detection and monitoring cycle mechanism. In addition, we explore the possibility of moving towards the high-quality development goal of safety helmet safety protection from the perspective of performance change and practice deduction. Safety helmet testing aims to provide mine workers with standard-compliant safety protection. The concerned management strives to continuously improve the quality of safety helmet detection and improve the safety level of the mine. To this end, we have taken a series of measures, including strengthening safety helmet control based on the improved YOLOv5 quality inner loop, building a mechanism for interaction and feedback between safety helmet and mine workers' quality perception, and establishing an evaluation system for safety helmet testing and working environment. In this way, we can realize the high-quality development of safety helmet detection, improve mine safety management, and meet the needs of workers.