Computer Science ›› 2018, Vol. 45 ›› Issue (3): 268-273.doi: 10.11896/j.issn.1002-137X.2018.03.043

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License Plate Detection Based on Principal Component Analysis Network

ZHONG Fei and YANG Bin   

  • Online:2018-03-15 Published:2018-11-13

Abstract: License plate recognition is the core technology of intelligent transportation system (ITS).License plate detection is a crucial step in the license plate recognition technology.Since only low-level artificial features are used to achieve license plates detection in most traditional methods,the detection error rates are usually low in complex scenes.In this paper,a novel license plate detection method based on principal component analysis network (PCANet) was proposed.Firstly,the license plate candidate area is marked with Sobel operator based edge detection and edges symmetry analysis.Secondly,by inputting candidate area into PCANet,the deep feature extraction is peformed for candidate area in PCANet and the support vector mechine is used to confirm the license plate.Finally,an efficient non maximum suppression (NMS) is used to label the best license plate detection area.For performance evaluation,a dataset consisting of images in various scenes was used to test the proposed method,and the results were also compared with those of traditional methods.The experimental results show the robustness of the proposed algorithm,and its performance is also superior to the traditional method of license plate detection.

Key words: License plate detection,PCANet,Feature extraction,NMS

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