Automatic Number Plate Recognition Using Deep Learning Under Night time and Low-Illumination Conditions
DOI:
https://doi.org/10.21015/vtse.v14i1.2303Abstract
Intelligent traffic management relies heavily on the recognition and localization of license plate numbers of moving vehicles, making it a critical task in this field. Numerous methods have been proposed to automate this procedure, utilizing computer vision and image processing algorithms to extract the number and characters from the detected license plate in surveillance photos and videos. However, these methods have primarily focused on daytime photographs and films, neglecting the challenges posed by difficult weather conditions or dim lighting settings. As a result, identifying the position of license plates and interpreting the characters from them remains an understudied area, particularly in low-light environments and night time photography. In response, we present a Night Time number plate detector and recognizer model in this paper. The model begins with a YOLOv5-based detector that has been trained to detect license plates in dark and hazy vehicle photos, generating a polygon bounding box around the number plate. The second phase of the process comprises an improvement module, where the retrieved picture of the license plate undergoes a variety of filters. Lastly, Easy OCR is employed to read the characters on the license plate. Our experimental results demonstrate that training the detector on dark and low illumination photographs, along with precise bounding box generation, significantly improves detection and recognition accuracy. Specifically, our model achieved a mAP score of 97%, highlighting the efficacy of our approach. In conclusion, our Night Time number plate detector and recognizer model represents a significant step forward in the recognition and localization of license plate numbers, particularly in low-light conditions. Our approach provides a powerful and effective tool for intelligent traffic management systems, and we believe that our results will pave the way for further research in this field.
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