Abstract: Traditional image recognition and deep learning models are difficult to detect license plates in complex environments.Their scene applicability and accuracy are low,which seriously threatens traffic safety and affects the development of intelligent transportation.This paper proposes a complex environment license plate recognition algorithm based on improved image enhancement and CNN.First,after calculating the average gray value of the target image,we use the ACE algorithm and the dark channel prior dehazing algorithm to perform image enhancement on the license plate dataset in complex environments.Then,a license plate area localization algorithm that combines the key features of color and the peak is proposed,effectively locating the license plate area by eight-core steps in a complex environment.Finally,a five-layer convolutional neural network model is constructed to recognize the license plate character.Experimental results show that the proposed algorithm can effectively identify the license plates of vehicles in complex environments.The precision of the algorithm’s license plate area location in complex environments is 86.04%,the recall is 82.60%,and the F₁-score is 84.29%.Among them,the F₁-score of the proposed algorithm is 47.29% higher than the traditional image processing algorithm,24.73% higher than the SSD algorithm,26.37% higher than the YOLO algorithm and 17.15% higher than the YOLOv3 algorithm.At the same time,the time complexity of the proposed method is low,and it belongs to a lightweight license plate recognition method.Also,it can eliminate noise and realize license plate character re-cognition.Therefore,it has specific application prospects and practical value and provides a theoretical basis for intelligent transportation research.

Key words: License plate recognition, Image enhancement, Deep learning, Complex environment, Intelligent transportation