Investigating into Deep Neural Networks for Applicable Hard-hat Wearing Detection in Substations

Abstract

All staffs are strictly requested to wear hard-hats when working in substations. Various object detection algorithms, especially those based on deep learning, thus have been proposed for the corresponding purpose. A deep learning based-object detection algorithm commonly involves a fundamental neural network which dominates detection performances, so this paper investigates different types of networks’ applicability when utilizing them with a typical object detection algorithm for the monitoring of hard-hat wearing in substations. This is conducted from various perspectives concerned by real-world implementation that includes time consumption, computation speed, precision and more. As a consequence, this study provides a guideline to the selection of the most appropriate deep neural network architectures for the specific monitoring scenario.