关键词: 目标检测, 交通标志, YOLOv3, 数据增强, 大尺度预测

Abstract:

Aiming at solving the problems of small size, low resolution and insignificant features in traffic sign targets detection, an improved network model of YOLOv3 is proposed. After using the color enhancement method to enhance the traffic sign data, the FPN structure in the original network is improved, retaining the large-scale prediction with a scale of 52×52 in the original network, then it builds a larger-scale prediction with a scale of 108×108 by using the feature map of the second down-sampling output in the YOLOv3 network. In order to solve the problem of image size and distortion, it uses pooling operations with fixed block sizes of 5, 9, and 13 before the detection layer. And then, the separately output features are merged with the original feature map to achieve the same output size for different input sizes. Finally, the K-means clustering algorithm is used to cluster the TT100K traffic sign data set, the initial candidate box of the network is redefined, and the YOLOv3 network model, the improved YOLOv3 network model and other small target detection algorithms are used to compare experiments on the TT100K data set. The results show that the improved YOLOv3 network model can detect traffic signs more effectively, and the average detection accuracy of the improved YOLOv3 network model is 8.3%, 6.1% and 4.3% higher than that of the original YOLOv3 network model at three scales. When the FPS changes little, the recall rate and accuracy are significantly improved. At the same time, the improved YOLOv3 algorithm has better detection accuracy and real-time performance than other small target detection algorithms.

Key words: object detection, traffic sign, YOLOv3, data enhancement, large-scale prediction