改进的YOLOv3交通标志识别算法
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作者:
作者单位:

1.长江大学计算机科学学院;2.三峡大学

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家科技重大专项项目(2016ZX05055)


Improved YOLOv3 Traffic Sign Recognition Algorithm
Author:
Affiliation:

1.School of computer science,Yangtze University;2.Three Gorges University

Fund Project:

National Science and technology major special projects

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    摘要:

    针对复杂场景下交通标志检测存在精度低、检测速度慢等问题,提出一种基于YOLOv3改进的S-YOLO交通标志算法。首先,合并批归一化层到卷积层,以提升模型前向推理速度;其次,采用二分K-means聚类算法,确定适合交通标志的先验框;然后引入空间金字塔池化模块,提取特征图深度特征;最后引入CIoU回归损失函数,提升模型检测精度。实验结果表明,在重制的CTSDB交通标志数据集下,所提算法与YOLOv3相比,平均准确率和检测速度分别提升了4.26%和15.19%,同时相较YOLOv4以及其他算法对交通标志识别有更优的精度和速度,具有良好的鲁棒性,满足复杂场景高效实时检测。

    Abstract:

    Aiming at the problems of low accuracy and slow detection speed in traffic sign detection in complex scenes, an improved S-YOLO traffic sign algorithm based on YOLOv3 is proposed. Firstly, the batch normalization layer is merged into the convolution layer to improve the forward reasoning speed of the model; Secondly, binary K-means clustering algorithm is used to determine the a priori frame suitable for traffic signs; Then the spatial pyramid pooling module is introduced to extract the depth features of the feature map; Finally, CIoU regression loss function is introduced to improve the detection accuracy of the model. The experimental results show that under the reproduced CTSDB traffic sign dataset, the mAP and FPS of the proposed algorithm are improved by 4.26% and 15.19% respectively compared with YOLOv3. At the same time, compared with YOLOv4 and other algorithms, the proposed algorithm has better accuracy and speed for traffic sign recognition, has good robustness, and meets the efficient real-time detection of complex scenes.

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引用本文

林轶,陈琳,王国鹏,等. 改进的YOLOv3交通标志识别算法[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-12-08
  • 最后修改日期:2022-05-03
  • 录用日期:2022-05-13
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