Abstract
The traffic block port monitors and manages the road traffic by shooting and recording the motor vehicles. However, due to the complex factors such as shooting angle, light condition, environmental background, etc., the recognition rate of license plate is not high enough. High light and low light under complex lighting conditions are symmetry problems. This paper analyzes and solves the low light problem in detail, an image adaptive enhancement algorithm under low light conditions is proposed in the paper. The algorithm mainly includes four modules, among which, the fast image classification module uses the deep and separable convolutional neural network to classify low-light images into low-light images by day and low-light images by night, greatly reducing the computation burden on the basis of ensuring the classification accuracy. The image enhancement module inputs the classified images into two different image enhancement algorithms and adopts the idea of dividing and ruling; the image quality evaluation module adopts a weighted comprehensive evaluation index. The final experiment shows that the comprehensive evaluation indexes are all greater than 0.83, which can improve the subsequent recognition of vehicle face and license plate.
Highlights
As the inventory of motor vehicles in China increases, it has brought a lot of convenience to people’s production and life
The above methods for noise reduction filtering of binary image improve the accuracy of character segmentation to a certain extent, they have little significance for license plate area detection and vehicle face recognition, especially after the advent of deep learning correlation algorithms
Pathosis refers to that the contrast and brightness of the images are both so low that the naked eye can only see the front lamp of the vehicle but cannot distinguish the vehicle face and license plate
Summary
As the inventory of motor vehicles in China increases, it has brought a lot of convenience to people’s production and life. The huge traffic flow has brought problems such as traffic jams, frequent traffic crime incidents, etc. Problems such as motor vehicle theft, traffic violation, fake plate vehicles, and license plate number covering, etc., have plagued the Traffic Control. The intelligent traffic system (ITS), being modern traffic management, urgently needs to solve these problems. It has become an important development direction for ITS to integrate intelligent image processing technology based on machine vision and pattern recognition into ITS closely with advanced hardware technology so as to improve the efficiency of traffic management [1].
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