关键词: RPN网络, LBP特征, 神经网络, 机器视觉, 目标检测

Abstract: With the large-scale promotion of electric vehicles in the world, people pay more and more attention to the automation of charging for electric vehicles. In the process of automatic charging, the key step is the detection and identification of charging socket, and the completion of docking and plugging between charging socket and charging gun. This paper proposes a charging socket detection and recognition algorithm based on Faster-RCNN, improves the RPN network part by combining the saliency image, strengthens the charging port area in the images, and uses the processed feature image as the input of RPN network. A multi-scale MB-LBP feature is designed to classify the candidate regions with neural network. The training and testing on the self-built dataset are conducted based on the Pytorch framework. The experimental results show that the proposed algorithm can meet the needs of the work scene and deal with the changes of illumination and seale.

Key words: RPN net, LBP feature, neural network, machine vision, target detection