Vehicle-logo recognition based on modified HU invariant moments and SVM
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As a part of the vehicle identification system, the logo recognition, while matching with the license plate recognition, can be used to define the identity of the vehicle more accurately and provide reliable evidence for the deck car investigation, illegal escape and vehicle tracking. However, it is a difficult problem for the research to position the logos of different vehicles and the identification of the vehicles under low illumination conditions. This paper firstly uses the features of the color of the license plate to locate the license plate, and carries out the rough location of the logo according to the prior knowledge. Then, uses gray level, contrast enhancement, smoothing de-noising, edge detection and background suppression methods to deal with the coarse location of logo and realize the positioning of logo accurately. Next, extracts features of Vehicle-logo according seven HU invariant, considering the influence of low illumination conditions, this paper adds three HU invariant distances and establishes the characteristic library of the logo image. Thirdly, uses the support vector machine(SVM) to identify the logo and Cross validation(CV) methods to optimize the parameter C and g of SVM at the same time. In order to improve the recognition accuracy of the algorithm under low illumination conditions, the Grey Wolf Optimize (GWO) is used to further optimize the kernel function. Finally, takes 9 kinds of common Vehicle-logo as the logo to be identified, uses SVM to train 80% of the samples and test 20% of the samples. The results of experiments show that the increase of the invariant moments feature can obviously improve the accuracy of the logo, GWO is better than CV to improve the accuracy, and the average recognition rate is more than 92%, which effectively solve the problem of Vehicle-logo identification under low illumination conditions.
KeywordsVehicle-logo recognition Support vector machine Invariant moments Cross validation Grey wolf optimize
This work is supported by “the Fundamental Research Funds for the Central Universities (2016JBM053)”.
Jiandong Zhao and Xiaokang Wang presented the algorithms, analyzed the data and co-wrote the paper.
Compliance with ethical standards
Conflict of interests
The authors declare that there is no conflict of interests regarding the publication of this article.
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