Research on License Plate Recognition Algorithm in Hazy Weather Based on Deep Learning

Haze and fog weather decrease the accuracy of license plate recognition using machine vision. To address this issue, the paper proposes a multi-stage deep learning-based license plate recognition algorithm with fog detection and defogging. The algorithm utilizes an image average gradient-based fog detection model and an adaptive defogging processing approach with the improved ACE algorithm's adaptive color balancing fast algorithm to enhance the license plate region's visibility. Furthermore, image recognition and segmentation theory, edge detection algorithm, HSV color model, and mathematical morphology algorithm are applied to preprocess the license plate area. The convolutional neural network is then utilized to filter the license plate, enabling efficient license plate location and extraction. Finally, another convolutional neural network is employed for character recognition. The result shows a recognition accuracy of 98.7% in hazy weather and 99.3% in non-fog conditions with fast computation speed.