Objectives As an essential part of the modern intelligent transportation system, license plate recognition is of great social significance to improve the efficiency of the road networks and alleviate urban traffic pressure. however, it is still a great challenge to weak illumination license plate image recognition algorithm.
Methods A weak illumination license plate image enhancement network based on differential convolution neural network is constructed, the texture information of the license plate is decoupled into horizontal vertical and diagonal directions, and the texture of low illumination images in different scale-spaces is enhanced. In order to avoid local overexposure or low exposure of the enhancement results, this method uses the loss function of the YCbCr color space to optimize the model.
Results The results of image enhancement experiments show that compared with the traditional low-intensity image enhancement methods, the objective image quality result peak signal-to-noise ratio improves 0.47 dB.
Conclusions At the same time, the experimental results also prove the effectiveness of this algorithm in recognition of composite license plates and real scene license plates for the improvement of low-illumination image perception quality.
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