An Efficient Texture Descriptor for the Detection of License Plates From Vehicle Images in Difficult Conditions

This paper aims to identify the license plates under difficult image conditions, such as low/high contrast, foggy, distorted, and dusty conditions. This paper proposes an efficient descriptor, multi-level extended local binary pattern, for the license plates (LPs) detection system. A pre-processing Gaussian filter with contrast-limited adaptive histogram equalization enhancement method is applied with the proposed descriptor to capture all the representative features. The corresponding bins histogram features for a license plate image at each different level are calculated. The extracted features are used as the input to an extreme learning machine classifier for multiclass vehicle LPs identification. The dataset with English cars LPs is extended using an online photo editor to make changes on the original dataset to improve the accuracy of the LPs detection system. The experimental results show that the proposed method has a high detection accuracy with an extremely high computational efficiency in both training and detection processes compared to the most popular detection methods. The detection rate is 99.10% with a false positive rate of 5% under difficult images. The average training and detection time per vehicle image is 4.25 and 0.735 s, respectively.