Vehicle License Plate Recognition With Deep Learning

Chi-Hsuan Huang, Yu Sun, Chiou-Shana Fuh
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Abstract

In this chapter, an AI (artificial intelligence) solution for LPR (license plate recognition) on moving vehicles is proposed. The license plates in images captured with cameras on moving vehicles have unpredictable distortion and various illumination which make traditional machine vision algorithms unable to recognize the numbers correctly. Therefore, deep learning is leveraged to recognize license plate in such challenging conditions for better recognition accuracy. Additionally, lightweight neural networks are chosen since the power supply of scooter is quite limited. A two-stage method is presented to recognize license plate. First, the license plates in captured images are detected using CNN (convolutional neural network) model and the rotation of the detected license plates are corrected. Subsequently, the characters are recognized as upper-case format (A-Z) and digits (0-9) with second CNN model. Experimental results show that the system achieves 95.7% precision and 95% recall at high speed during the daytime.
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车辆牌照识别与深度学习
在本章中,提出了一种基于移动车辆车牌识别的人工智能解决方案。移动车辆上的摄像头拍摄的车牌图像具有不可预测的失真和不同的光照,这使得传统的机器视觉算法无法正确识别车牌数字。因此,在这种具有挑战性的条件下,利用深度学习来识别车牌,以获得更好的识别精度。此外,由于滑板车的电力供应相当有限,因此选择了轻量级神经网络。提出了一种两阶段车牌识别方法。首先,利用CNN(卷积神经网络)模型对采集图像中的车牌进行检测,并对检测到的车牌旋转进行校正;随后,使用第二个CNN模型将字符识别为大写格式(A-Z)和数字(0-9)。实验结果表明,该系统在白天高速运行时准确率达到95.7%,查全率达到95%。
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