基于改进YOLOv3的交通标志检测
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作者单位:

1.四川大学计算机学院;2.四川大学视觉合成图形图像技术重点学科实验室

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TP39

基金项目:

国家重点研究与发展计划基金(2018YFC0830300); 国家自然科学基金(61571312)


Traffic sign detection based on improved YOLOv3
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Affiliation:

1.College of Computer Science, Sichuan University;2.State Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University

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    摘要:

    针对交通标志检测小目标数量多、定位困难及检测精度低等问题,本文提出一种基于改进YOLOv3的交通标志检测算法. 首先,在网络结构中引入空间金字塔池化模块对3个尺度的预测特征图进行分块池化操作,提取出相同维度的输出,解决多尺度预测中可能出现的信息丢失和尺度不统一问题;然后,加入FI模块对3个尺度特征图进行信息融合,将浅层大特征图中包含的小目标信息添加到深层小特征图中,从而提高小目标检测精度.针对交通标志数据集特点,使用基于GIoU改进的TIoU作为边界框损失函数替换MSE函数,使得边界框回归更加准确;最后,通过k-means++算法对TT100K交通标志数据集进行聚类分析,重新生成尺寸更小的候选框. 实验结果表明,本文算法与原始YOLOv3算法相比mAP提升11.1%,且检测每张图片耗时仅增加6.6 ms,仍符合实时检测要求. 与其他先进算法相比,本文算法具有更好的检测精度和检测速度.

    Abstract:

    Aiming at the problems of traffic sign detection such as large number of small targets, difficult location and low detection accuracy, a traffic sign detection algorithm based on YOLOv3 is proposed. First, the spatial pyramid pooling module is introduced into the network structure to perform the block pooling operation on three prediction feature maps with different scales, and the output of the same dimension is extracted, so as to solve the problem of information loss and scale disunity that may occur in the multi-scale prediction. In order to improve the detection accuracy of small target, the FI module is added to carry out information fusion of the three scale feature maps, and the small target information contained in the shallow large feature map is added to the deep small feature map. According to the characteristics of traffic sign dataset, the improved TIoU based on GIoU is used as the boundary box loss function to replace MSE function, which makes the boundary box regression more accurate. Finally, k-means++ algorithm is used to perform clustering analysis on the TT100K traffic sign dataset, generating new anchors with smaller size. Experimental results show that the proposed algorithm improves mAP by 11.1% compared with the original YOLOv3 algorithm, and the detection time of each image only increases by 6.6ms, which still meets the real-time detection requirements. Compared with other advanced algorithms, the proposed algorithm has better detection accuracy and detection speed.

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引用本文格式: 王卜,何扬. 基于改进YOLOv3的交通标志检测[J]. 四川大学学报: 自然科学版, 2022, 59: 012004.

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  • 收稿日期:2021-03-02
  • 最后修改日期:2021-05-19
  • 录用日期:2021-05-25
  • 在线发布日期: 2022-01-19
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