Integrating YOLOv8 and CSPBottleneck based CNN for enhanced license plate character recognition

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Real-Time Image Processing Pub Date : 2024-09-10 DOI:10.1007/s11554-024-01537-2

Sahil Khokhar, Deepak Kedia

引用次数: 0

Abstract

The paper introduces an integrated methodology for license plate character recognition, combining YOLOv8 for segmentation and a CSPBottleneck-based CNN classifier for character recognition. The proposed approach incorporates pre-processing techniques to enhance the recognition of partial plates and augmentation methods to address challenges arising from colour diversity. Performance analysis demonstrates YOLOv8’s high segmentation accuracy and fast processing time, complemented by precise character recognition and efficient processing by the CNN classifier. The integrated system achieves an overall accuracy of 99.02% with a total processing time of 9.9 ms, offering a robust solution for automated license plate recognition (ALPR) systems. The integrated approach presented in the paper holds promise for the practical implementation of ALPR technology and further development in the field of license plate recognition systems.

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整合 YOLOv8 和基于 CSPBottleneck 的 CNN，增强车牌字符识别能力

本文介绍了一种车牌字符识别综合方法，将用于分割的 YOLOv8 与用于字符识别的基于 CSPBottleneck 的 CNN 分类器相结合。所提出的方法结合了预处理技术来提高部分车牌的识别率，并结合了增强方法来应对颜色多样性带来的挑战。性能分析表明，YOLOv8 的分割准确率高，处理时间短，同时还具有精确的字符识别能力和 CNN 分类器的高效处理能力。集成系统的总体准确率达到 99.02%，总处理时间仅为 9.9 毫秒，为车牌自动识别 (ALPR) 系统提供了强大的解决方案。本文介绍的集成方法为 ALPR 技术的实际应用和车牌识别系统领域的进一步发展带来了希望。

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来源期刊

Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

6.80

自引率

6.70%

发文量

审稿时长

6 months

期刊介绍： Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.