A Detection Method of Traffic Scene License Plate for Data Desensitization

Rapid and accurate detection of license plates in vehicle-based images is significant for protecting privacy information in smart transportation. However, the original YOLOv8 algorithm has limitations on the license plate detection in traffic scenes, such as weak feature extraction ability of small targets and misdetection of background information, etc. To fill these gaps, an improved traffic scene license plate detection method based on YOLOv8 (TLP-YOLO) is proposed. The efficient multi-scale attention (EMA) module is adopted to enhance the ability of the backbone network to extract image characteristics. It makes the backbone network pay more attention to target regions of different scales and improves the recognition ability of the model to background information. A new feature pyramid network with skip connection and weighted fusion (SW-FPN) is designed. It enriches the features of small targets and avoid the information loss between different levels of the feature pyramid network, which improving the multi-scale feature fusion ability. In order to reduce the floating-point operations (FLOPs) and maintain the detection accuracy, the partial convolution (PConv) and pointwise convolution (PWConv) modules are introduced to replace the conventional convolution structure in detection head, which reduces redundant calculations and improves the utilization efficiency of spatial features. Based on Chinese city parking dataset (CCPD) and Chinese road plate dataset (CRPD), a dataset with multiple traffic scenes is constructed to verify the property of the model. Experimental results show that: ①The average precision (IOU changes from 0.5 to 0.95) of the proposed network is 83.6%, which is 2% higher than that of YOLOv8. The average precision (IOU is 0.7) of the proposed network is 97.7%, which is 0.8% higher than that of YOLOv8. ②The FLOPs of TLP-YOLO model is 7.5 G, the number of parameters is 1.67 M, and the detection speed reaches 101 fps. In comparison to the original YOLOv8, the FLOPs and the number of parameters is reduced by 8% and 45%, the detection speed is about the same. The improved algorithm can not only ensure the lightweight of the model, but also meet the requirements of vehicle equipment for the accuracy and deployment of license plate detection in traffic scenes.