基于伪样本正则化Faster R⁃CNN的交通标志检测

doi:10.13229/j.cnki.jdxbgxb20200956

Abstract

Abstract:

Traffic sign detection is the key step of a traffic sign recognition system. To solve the problems of only detection specific categories of traffic signs and over?fitting due to the lack of training samples in deep learning based methods， we propose a deep learning based traffic sign detection method ? pseudo sample regularization Faster R?CNN. In our method， we first design a pseudo sample regularization scheme using the traffic signs and unlabeled background samples from the training set. Then， the region proposal network （RPN） in the deep learning framework is used to generate region proposals. At last， the RPN network and Fast R?CNN detection network are working alternatively and jointly using the alternative training， shared CNN and co?training strategies to obtain the Faster R?CNN traffic sign detection model. We demonstrate the effectiveness of the proposed method through comprehensive experimental analysis. The results show that our method shows boosted traffic sign detection performance and the over?fitting problem can be suppressed。

Key words: information processing technology, traffic sign detection, pseudo sample regularization, faster region?convolutional neural networks （Faster R?CNN）, region proposal networks

CLC Number:

TN911.73

Hou⁃jie LI,Fa⁃sheng WANG,Jian⁃jun HE,Yu ZHOU,Wei LI,Yu⁃xuan DOU. Pseudo sample regularization Faster R⁃CNN for traffic sign detection[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1251-1260.

Figures/Tables 8

Fig.1

Table 1

Fig.2

Table 2

Comparison of detection performance in the case of various regularization （AP）"

禁止类	警告类	指示类	其他类	$m A P$
90.85	99.59	99.247	100	97.42
99.89	90.91	98.64	100	97.35
99.83	99.09	99.81	100	99.68
100	99.72	99.45	100	99.79

Table 2

Fig. 3

Table 3

Comparison of various detection methods on GTSDB （AP）"

方法	禁止类	警告类	指示类	其他类	$m A P$
［26］	100	99.91	100.00	?	?
［27］	100	98.85	92.00	?	?
［13］	99.98	98.85	95.76	?	?
［28］	99.29	97.13	96.74	?	?
［29］	?	99.73	97.62	?	?
［23］	96.81	96.12	94.02	?	?
［24］	99.89	99.90	99.16	?	?
本文方法	99.83	99.09	99.82	100%	99.68%

Table 3

Fig.4

Fig.5

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