An Improved Detection Method for Dressing Behavior of Substation Workers Based on SSD
Authors: 
Mingzeng Zhu, Xiaolan Liu, Hongyu Zhou, Kaibo Hu, Xinhua Huang, Hao QiuAuthors Info & Claims
Pages 597 - 600
Abstract
In the process of safe production, it is necessary to carry out rapid and accurate detection and timely warning of personnel's safe dress and operation to reduce casualties and property losses. However, the existing detection methods for personnel dressing and construction operations have problems such as slow detection speed and low detection accuracy. To solve such problems, an improved rapid detection algorithm for substation on-site operation based on single shot detector (SSD) is proposed. The convolution neural network vgg-16 of SSD detection algorithm was replaced by the lightweight convolution neural network mobile netv3-small to reduce the model parameters and improve the detection rate. At the same time, the feature pyramid network structure is used to fuse the more abstract features in the deep layer with the more detailed features in the shallow layer to improve the detection accuracy. Training and testing experiments are carried out by making the helmet data set HWear independently, and data enhancement technology is used to improve the detection performance of the model during training. The experimental results show that the improved SSD method improves the risk detection rate of substation field operation to 108 fps. And at the same time, compared with the average accuracy of traditional SSD algorithm, it is increased by 0.5%, which has certain practical significance.
References
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Published In
December 2020
687 pages
ISBN:9781450388665
DOI:10.1145/3452940
Copyright © 2020 ACM.
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Association for Computing Machinery
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Published: 17 May 2021
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ICITEE2020
ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering
December 3 - 5, 2020
Hunan, Changde City, China
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