基于两阶段分类算法的中国交通标志牌识别

doi:10.19562/j.chinasae.qcgc.2022.03.016

Abstract

Abstract:

Autonomous driving technology plays an important role in alleviating traffic congestion and reducing transportation cost. Advanced driver assistance systems （ADAS） can effectively increase the comfort and safety of car driving. Traffic signs contain rich semantic information， which provides important constraints for decision-making of autonomous vehicles and ADAS. Therefore， development of traffic sign recognition algorithms is very important. Based on the characteristics of traffic scenes in China and the high accuracy requirements of automatic driving and ADAS on traffic sign detection， this paper proposes a traffic sign recognition algorithm framework based on two-stage classification. The algorithm consists of two stages of recognition and classification. In the recognition stage， traffic signs in the image are detected. And in the classification stage， traffic signs are divided into categories and subcategories. By refining the task， the algorithm improves the performance of each algorithm module independently， thus improving the recognition accuracy of the whole algorithm. In this paper， the single-stage recognition algorithm is improved to be used as the recognition module of the algorithm. The experimental results show that the proposed algorithm is better than the benchmark single-stage recognition algorithm in accuracy， with an average increase of 8.52% in mAP. In addition， with the detection speed better than the traditional two-stage recognition algorithm Faster-RCNN， the mAP is improved by 40%.

Key words: object recognition, traffic sign recognition, convolutional neural network

Runze Feng,Kun Jiang,Weiguang Yu,Diange Yang. Chinese Traffic Sign Recognition Based on Two-stage Classification Algorithm[J].Automotive Engineering, 2022, 44(3): 434-441.

Figures/Tables 12

References 32

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数据集	年份	图片数	实例数	种类
GTSDB	2013	900	1 200	3
TT100K	2016	10 000	300 00	128
CCTSDB	2017	15 723	21 134	3

模块	操作	层数
1	Conv3×3	1
2	MBConv1， k3×3	2
3	MBConv6， k3×3	3
4	MBConv6， k5×5	3
5	MBConv6， k3×3	6
6	MBConv6， k5×5	6
7	MBConv6， k5×5	6
8	MBConv6， k3×3	1
9	Conv1×1&池化&全连接	1

模型	准确率/%	参数量/10⁶	浮点数运算/10亿
EifficientNet-B3	81.6	12	1.8
ResNet-101	80.9	84	32
PolyNet	81.3	92	35

数据集	图片数	实例数	实例数多于100的种类数
TT-100K	15 723	21 134	45
CCTSDB	10 000	30 000	3
本文数据集	18 955	41 028	53

算法	mAP@0.5/%	准确率/%	召回率/%	误检率/%	检测时长/（ms·帧^-1）	帧率/（帧·s^-1）
Faster RCNN	33.52	89.89	56.79	23.22	74.57	13
YOLOv3	72.05	92.97	75.22	12.09	36.96	27
YOLOv4	72.28	92.36	75.67	11.91	41.38	24
YOLOX	68.13	90.17	65.51	17.42	48.35	20
two-stage-YOLOv3	78.74	94.41	85.34	6.44	28.03+37.13	15
two-stage-YOLOv4	79.83	94.40	84.78	6.23	35.54+37.13	14
Two-stage-YOLOX	79.45	94.80	82.81	6.40	42.36+37.13	13

Chinese Traffic Sign Recognition Based on Two-stage Classification Algorithm

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