Abstract: To address the problem that existing license plate detection algorithms are ineffective in complex environments， a deep learning-based GEG-YOLOv4 lightweight license plate detection model is proposed. The model uses YOLOv4 as the basic framework and GhostNet as the backbone network to significantly reduce the number of model parameters， and incorporates an ECA attention module that can avoid dimensionality reduction and effectively capture cross-channel interaction information to increase the channel weight of license plate information and reduce the interference of license plate information from the complex environment background. Finally， the Ghost module is used to replace some of the normal convolution in the deep network， which further reduces the number of model parameters while better preserving the redundant information in the feature map. The experimental results on a large license plate dataset CCPD show that the GEG-YOLOv4 model reduces the number of parameters by about 88%， increases the AP value by 0.09% and improves the speed by about 55% compared with YOLOv4， which has better detection performance for license plate data in complex environments than other methods and can meet the needs of practical application scenarios.

Key words: license plate detection, deep learning, complex environment, lightweight, YOLOv4