Research On License Plate Recognition Of Mine Loadometer Based On Deep Learning

In the coal industry,enterprises need to quickly and accurately manage the coal materials during the transportation and storage of coal.The mining loadometer is an effective weighing instrument and plays a very important role in the transportation process of coal.As the key identity information of vehicles,in order to improve the accuracy of measurement and management of transportation vehicles,it is of great significance to study the license plate recognition of mining loadometer.This paper has conducted in-depth research on the license plate recognition technology in the mining loadOmeter system.Through research,it is found that the traditional license plate recognition technology has strong dependence between modules,which easily leads to the accumulation of errors and affects the final recognition result.Moreover,due to the special nature of the coal industry,vehicles that transport coal resources for a long time are likely to cause the license plate to be contaminated with stains such as coal ash and slime,and it is easy to cause the license plate to wear out,which makes the license plate characters blurred.If the research is carried out by the traditional license plate recognition method,complex pretreatment of the mining loadometer license plate image is required.Through comprehensive analysis,this paper uses the deep learning method to divide the mining loadometer license plate recognition into two processes:license plate detection and license plate character recognition.There is no need to correct the license plate and character segmentation,and there is no need to perform complex preprocessing operations on the image.After analysis,this paper improves the YOLOv3 network according to the characteristics of the mining loadometer license plate,making it better suited for the detection of mining loadometer scale license plates.In the license plate character recognition problem,since the license plate detection algorithm of this paper inputs the entire image containing the license plate,in the license plate detection stage,the neural network not only learns the characteristics of the license plate,but also learns the characteristics of the characters in the license plate.Based on this,this paper adopts the idea of migration learning to adjust and improve the backbone network of the trained license plate detection network,DarkNet-53,and then use it to identify the license plate characters of mine loadometers,which is beneficial to accurately identify the license plate after a small number of trainings.In order to support the training of the deep learning license plate recognition model,this paper uses artificial simulation to generate a large number of license plate images.In this paper,through the in-depth study of the license plate recognition of mining loadometer,the proposed mining loadometer license plate recognition method has better robustness to special situations such as night and license plate tilt,relatively improve the reliability and automation of the mining loadometer system,it has certain theoretical significance and engineering value.