Traffic sign recognition algorithm based on depthwise separable convolutions

Jin-sheng YANG; Yan-nan YANG; Tian-jiao LI

doi:10.3788/YJYXS20193412.1191

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Traffic sign recognition algorithm based on depthwise separable convolutions

Image Processing | 更新时间：2020-07-16

- Traffic sign recognition algorithm based on depthwise separable convolutions
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 12, Pages: 1191-1201(2019)
- 作者机构：
  
  天津大学微电子学院, 天津 030072
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193412.1191
  CLC： TP391
- Received：26 August 2019，
  
  Accepted：26 September 2019，
  
  Published：05 December 2019
- 稿件说明：
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Jin-sheng YANG, Yan-nan YANG, Tian-jiao LI. Traffic sign recognition algorithm based on depthwise separable convolutions[J]. Chinese journal of liquid crystals and displays, 2019, 34(12): 1191-1201.
DOI：

Jin-sheng YANG, Yan-nan YANG, Tian-jiao LI. Traffic sign recognition algorithm based on depthwise separable convolutions[J]. Chinese journal of liquid crystals and displays, 2019, 34(12): 1191-1201. DOI： 10.3788/YJYXS20193412.1191.

摘要

针对目前实时交通标志识别中出现的对于中小型目标检测精度低的问题，本文提出了一种基于YOLOv3-tiny的深度可分离卷积（Depthwise Separable Convolutions）的轻量级交通标志检测网络。采用深度可分离卷积构建深度可分离卷积模块代替普通卷积搭建特征提取网络，在保证计算量的前提下更好地提取中小型目标的特征信息。同时，改进多尺度特征融合网络，提高对中小型交通标志的检测精度，使用h-swish激活函数减少因为网络层数增加而丧失的图像特征，实现对多类交通标志的检测。实验结果表明：本算法有效的提高了对中小型交通标志的检测，在验证集上对警告标志（Warining）指示标志（Mandatory）、禁止标志（Prohibitory）3类交通标志进行检测，检测精度（AP）结果分别为98.57%，96.03%，98.04%。检测平均精度（mAP）97.54%、检测速度为201.5 f/s.平均精度较YOLOv3-tiny提高了14.01%。在保证轻型网络的计算量低、检测时效性好的前提下，有效地提升了检测精度。

Abstract

In order to solve the problem of low precision detection for small and medium-sized targets in the real-time traffic sign recognition

a lightweight traffic sign detection network based on YOLOv3-tiny depthwise separable convolution is proposed. In this paper

a deep separable convolution module is constructed by using deep separable convolution instead of the common convolution to construction feature extraction network

the feature information of small and medium-sized targets is better extracted under the premise of ensuring the calculation amount.At the same time

the multi-scale feature fusion network is improved to improve the detection accuracy of small and medium-sized traffic signs. The h-swish activation function is used to reduce the image features lost due to the increase of the number of network layers

and the detection of multiple types of traffic signs is realized.The experimental results show that the algorithm effectively improves the detection of small and medium-sized traffic signs. Warining

mandatory and prohibitory traffic signs are detected on the verification set. The detection accuracy (AP) is 98.57%

96.03% and 98.04% respectively. The average detection accuracy (mAP) was 97.54% and the detection speed was 201.5 f/s. The average accuracy was 14.01% higher than that of YOLOv3-tiny. The algorithm effectively improves the detection accuracy under the premise of ensuring low calculation of light network and good timeliness of detection.

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Keywords

references

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