Research and Application of Tunnel Lighting State Recognition Technology Based on Histograms of Oriented Gradients Features

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.