Development of Hard Hat Wearing Monitoring System Using Deep Neural Networks with High Inference Speed

Personal protection equipment is essential for safe working conditions on a construction sites and other industrial enterprises. However, there is a number of employee who violate safety precautions, thus, taking additional risks. To avoid accidents strict supervision should be applied. In this work an automatic hard hat wearing detection system from surveillance cameras is developed. To save computational resources small neural networks with high inference speed are applied. For the training of neural networks, a dataset consisting of several industrial scenes and 1478 images was composed. Frame based precision of SqueezeDet neural network for object detection was increased by 9% with the use of additional MobileNets classifier without significant decrease in operating speed. The overall system achieved 0.75 F1-score on a test dataset. Therefore, the developed hard hat wearing monitoring systems is suitable for enhancing on-site safety of workers.