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License Plate Recognition in Foggy Conditions: A Survey of Image Dehazing and License Plate Recognition Techniques
- 1 Department of Information Science and Engineering, Ocean University of China, Qingdao, Shandong, 266404, China
* Author to whom correspondence should be addressed.
Abstract
License plate recognition in foggy environments is one of the core challenges in Intelligent Transportation Systems (ITS). Fog causes blurred license plate images and reduced contrast, severely degrading recognition accuracy. This paper systematically reviews the key technologies for foggy license plate recognition, covering traditional and deep learning-based image dehazing methods, as well as advances in license plate localization and recognition algorithms, while analyzing their strengths and limitations. The study highlights that current methods face bottlenecks in dynamic fog density adaptation and generalization in extreme weather conditions. Future improvements require multi-modal data fusion and adaptive optimization to enhance performance. This work aims to provide theoretical references for optimizing and deploying license plate recognition technologies in foggy environments.
Keywords
License plate recognition, Image dehazing, Low visibility conditions, Dynamic fog density adaptation, Multi-modal data fusion, Intelligent Transportation Systems (ITS)
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Cite this article
Liang,J. (2025). License Plate Recognition in Foggy Conditions: A Survey of Image Dehazing and License Plate Recognition Techniques. Applied and Computational Engineering,163,1-6.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
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