License plate recognition is very important in intelligent transportation. Under the influence of uncontrollable conditions such as illumination, rain, snow, smog, blur and deformation, there are some difficulties and shortcomings in the field of license plate recognition. To solve this problem, this paper has made some improvements to YOLOV3 algorithm: 1. A K-means++ algorithm based on mean shift is proposed to select the anchor box. 2. CIOU is used as a regression function to make the fine-tuned detection result as close as possible to the ground truth box. 3. Adaptive spatial fusion is used for feature fusion, which avoids the conflict between features in the same layer and improves the efficiency of feature fusion. Experiments show that the improved algorithm has higher accuracy than the original algorithm.
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