Research on Electrostatic Discharge Behavior Detection Based on Deep Learning Video Recognition
Xinmiao Feng, Jianhong Sun, Xiaohua Xia, Xiaofei Gao, Shaobo Huo · 2024
In the modern industrial environment, the accumulation and discharge of static electricity pose potential threats to safe production. High-risk areas urgently require efficient and automated detection technologies for electrostatic discharge (ESD) behaviors. However, research on the automatic detection of workers’ ESD behaviors remains scarce. This paper proposes a video recognition method based on deep learning, utilizing RESNET50 for key point detection and an GRU network for long-term sequence behavior classification. By constructing a high-quality video dataset and systematically training and testing the model, we validate the effectiveness of this approach in detecting workers’ ESD behaviors. Experimental results demonstrate that the proposed method can efficiently and accurately identify various ESD behaviors, providing a reliable technical solution for industrial safety management regarding human ESD.