Open AccessInvestigating into Deep Neural Networks for Applicable Hard-hat Wearing Detection in Substations
Author(s) -
Tong Li,
Qipeng Chen,
Jinrui Gan,
Peng Wu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2002/1/012062
Subject(s) - object detection , artificial neural network , deep learning , artificial intelligence , computer science , object (grammar) , selection (genetic algorithm) , computation , machine learning , pattern recognition (psychology) , algorithm
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.