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

Al-Shemarry, Meeras Salman; Li, Yan; Abdulla, Shahab

doi:10.1109/tits.2019.2897990

Cited by 37 publications

(23 citation statements)

References 57 publications

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“…Thirdly, the performance comparison using the receive operating characteristic (ROC) curve between the SVM_MHOG_LBP and ELM_MHOG_LBP is provided. Finally, the detection accuracy and time efficiency of the proposed method are compared with the newest detection methods that also used the same database [25,44,45] as the one used in this study.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In this section, the proposed method is compared with the state-of-the-art license plate detection methods that used the same database of English car plates [25,44,45,50]. The performances are compared using the measures of the RR and PR rates as well as the F-m rate.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…Finally, the SVM is used to build the trained model to detect the LP regions and also an ELM classifier was used for evaluation purposes. An English car database which was reported by Al-Shemarry et al [25] was used in this study. This database contains many complicated vehicle images with different conditions, including low/high lighting, dusk, dirt, fog, and distorted images.…”

Section: Relevant Workmentioning

confidence: 99%

“…The proposed method is tested on two groups of databases: 1) An English car database including 510 vehicle images under different conditions [42]. 2) An extended English car database by Al-Shemarry et al [25] covering 1540 vehicle images under complicated conditions, such as too dark, blurry, distortion, and low/high contrast environments (see Fig. 14).…”

Section: Databasementioning

confidence: 99%

“…14). The second database is an extension of the first English car database as Al-Shemarry et al [25] changed the lighting, blurry, distortion conditions using online photo editor application to make it more challenging [43]. From the two databases, 530 vehicle images were randomly selected for the testing and 1520 were used for the training LPs dataset.…”

Section: Databasementioning

confidence: 99%

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Developing Learning-Based Preprocessing Methods for Detecting Complicated Vehicle Licence Plates

Al-Shemarry

2020

IEEE Access

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A licence plate detection (LPD) system is an important tool in several roadway traffic applications. This study aims to develop an advanced detection system that works well in complicated scenarios. It proposes a robust preprocessing enhancement method for accurately detecting the licence plates from difficult vehicle images. The proposed method includes the combination of a Gaussian filter, an enhancement cumulative histogram equalization method, and a contrast-limited adaptive histogram equalization technique. The local binary pattern and median filter with histogram of oriented gradient descriptors are used as powerful tools to extract key features from three types of licence plate resolutions. The extracted features are used as input to support vector machine classifier. Processing methods, such as a position-based method are used with the detector to reduce unwanted bounding boxes, as well as false positive values. Four databases consisting of 2050 vehicle images under different conditions are used. Various detection metrics, object localization, and the receiver operating characteristic (ROC) curve are used to evaluate the performance of the proposed method. The experimental results on vehicles databases in several languages, including English, Chinese, and Arabic number plates, show that the proposed method has achieved significant performance improvements. It outperforms the state-of-the-art approaches in terms of both the detection rate and the processing time. The detection rate when trained with 1520 LP images is 99.62% with a false positive rate of 1.675% for complicated images. The average detection time per vehicle image is 0.2408 milliseconds.

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Section: Experiments and Resultsmentioning

confidence: 99%