Intelligent monitoring model of wearing of industrial safety protective equipment based on YOLOv8 algorithm - Lin, Zaixin; Huang, Zihui; Yuan, Jiayou; Pan, Xinxin (2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 2023)
Industrial safety is essential for the reliability and sustainability of production operations. In this study, a safety protective equipment detection scheme based on YOLOv8 algorithm is proposed for industrial security scenarios. By optimizing the lightweight strategy and updating the weight of the original YOLOv8 model, a more efficient network architecture is adopted, which reduces the complexity of the model while ensuring detection accuracy, and deploys the lightweight model on edge applications. Experimental results show that the optimized lightweight model achieves 94% of the mAP50 values on the detection tasks of four types of safety protective equipment, and can be monitored in real time at a speed of 17 frames per second, achieving a good balance between accuracy and efficiency. This study provides a low-complexity, high-efficiency intelligent detection system scheme for industrial safety monitoring.