关键词: 目标检测, YOLOv3, 复杂场景, 车牌定位, CIoU, Inception-SE结构

Abstract:

Aiming at the problem of the difficulty of license plate positioning, slow detection speed and low detection accuracy in complex scenes such as lighting, multi-vehicle and low resolution, an improved method based on YOLOv3 is proposed. Firstly, the label information of the example is clustered by K-means++ method to obtain a new anchor size. And then, the improved thin feature extraction network（DarkNet41） is used to improve the detection efficiency of the model and reduce computational consumption. Moreover, multi-scale feature fusion is improved from 3-scale prediction to 4-scale prediction and improved Inception-SE structure is added to the detection network to improve the accuracy of detection. Finally, CIoU is selected as a loss function. The data is enhanced with the Multi-Scale Retinex（MSR） algorithm. Experimental analysis shows that the improved algorithm’s mAP reaches 98.84% and the detection speed reaches 36.4 frame/s, which has better accuracy and real-time performance compared with the YOLOv3 model and other algorithms.

Key words: target detection, YOLOv3, complex scenario, license plate location, CIoU, Inception-SE structure