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- Title
Parallelization of license plate localization on GPU platform.
- Authors
Akoushideh, Alireza; Shahbahrami, Asadollah; Joe Afshany, Abdorreza
- Abstract
Automatic license plate recognition which has many applications in intelligent transportation systems has three main steps, License Plate Detection (LPD), segmentation, and character recognition. The first step, LPD is the main step. This is because the accuracy of other steps depends on this step. While LPD has many challenging problems such as illumination changing, weather conditions, vehicle position, number of cars in each image, etc. In this paper, an algorithm for LPD is proposed. In the proposed technique, to remove noises from input images mean filtering is used. After that, the differences between the filtered image and input image are computed and it is converted to a black and white image. Finally, after applying edge detection and closing and opening morphological operations, license plate candidates are detected. The experimental results on a real Iranian dataset show that about 96.16% of license plates are localized. In addition, results show that the mean filter which has been used for noise removal is the most time-consuming kernel in the proposed algorithm. This important kernel on the GPU platform using CUDA parallel programming model is implemented. The experiments show that speedups of up to 14.54× and 10.77× for kernel- and application-level are achieved.
- Publication
Multimedia Tools & Applications, 2024, Vol 83, Issue 1, p2551
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-15656-8