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Research and Application of Tunnel Lighting State Recognition Technology Based on Histograms of Oriented Gradients Features
Technical Paper
2020-01-5192
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In order to ensure the safety of tunnel traffic and promote the digital
construction of tunnel lighting facility maintenance, this paper studies a
target detection and fault identification method based on intelligent video
surveillance. This method uses the luminance characteristics of the illuminator
in the image to preprocess the image. Then, the video image features of the lamp
are extracted as well as the improved HOG feature and SVM classifier are used
for training and recognition to realize the target detection and location of the
lamp. Subsequently, the current target equipment is identified, and finally,
combined with the equipment input signal, a fault identification model of the
lighting lamp that can distinguish the type of equipment fault is established,
so as to realize the real-time fault alarm. According to the results of the
experiment, Compared with the traditional HOG feature, the improved HOG feature
has higher recognition accuracy. This technology can provide comprehensive
applications such as equipment condition monitoring and intelligent fault
identification for the tunnel maintenance and operation system. In addition, the
technology can also be extended to other tunnel electromechanical equipment and
intelligent identification of tunnel infrastructure.
Authors
Citation
Ni, S., Zheng, Y., and Qi, L., "Research and Application of Tunnel Lighting State Recognition Technology Based on Histograms of Oriented Gradients Features," SAE Technical Paper 2020-01-5192, 2020, https://doi.org/10.4271/2020-01-5192.Data Sets - Support Documents
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References
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