Research on Intelligent Identification of Worker′s Unsafe Behavior in Urban Rail Transit Based on Convolutional Neural Network Algorithm
Journal Title: Urban Mass Transit - Year 2024, Vol 27, Issue 3
Abstract
[Objective] Worker′s unsafe behavior is the fundamental factor in urban rail transit construction accidents. As the traditional management mode is insufficient in restraining the workers from the unsafe behavior, it is necessary to eliminate the hidden danger of accidents subjectively with the help of high precision positioning and intelligent identification technologies. [Method] The generation mechanism of worker′s unsafe behavior in urban rail transit is introduced. In combination with the technologies of UWB (ultra-wideband) high precision positioning, camera self-calibration and intelligent identification based on convolutional neural network algorithm, an integrated intelligent management platform with functions of positioning, perception, identification, early warning and communication is built. Taking helmet identification as an example, the topology flow chart of helmet identification is constructed, and the algorithm of worker′s unsafe behavior identification based on convolutional neural network is tested. [Result & Conclusion] The test results show that the algorithm can identify the person who does not wear safety helmet on construction site, verifying its accuracy. The technology realizes intelligent identification and early warning of worker′s unsafe behavior in urban rail transit construction.
Authors and Affiliations
Fei GUO, Heng KONG, Guogang QIAO
Keywords
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- EP ID EP732817
- DOI 10.16037/j.1007-869x.2024.03.043
- Views 57
- Downloads 0
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