An improved Convolutional Neural Network used in abnormality identification of Indicating Lighting in Cable Tunnels

• Published in: 2018 International Conference on Power System Technology (POWERCON) pp. 4550 - 4554
• Main Authors: Pei, Xingyu, Huang, Shuntao, Cui, Jiangjing, Qiu, Wei, Liu, Danbing, Meng, Anbo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2018
• Subjects: Anomaly recognition; Convolution; convolution neural network; Feature extraction; Image edge detection; image processing; Lighting; Neural networks; Power cables; Sobel operator; Training
• DOI: 10.1109/POWERCON.2018.8602272

This paper proposes a method to identify the equipment anomalies based on convolution neural network, aiming at the weak-light situation inside the cable tunnel. This paper has proposed a method to identify the equipment anomalies with weak light situation inside the cable tunnel, based on the convolution neural network. On the basis of the gray image, this method adds the Sobel operator to enhance edge-preprocessing effect and start training through Convolution Neural Network (CNN). The convergence criteria is the Loss Function related with the Weight Parameter W and Bias Parameter b. The convergence method is the one named Backpropagation, which updates the parameters each time to reduce the loss. The fast operating speed of full connection layer can help getting the direct classification result of equipment status in the image. Based on the experimental analysis of the internal images of the Zhuhai tunnel, it can be seen that this method is suitable for the dark and chaotic environment of the tunnel. Additionally, it has a high recognition rate for the image segmentation of the lighting equipment and a high accuracy for the classification of the abnormal situation of the image.