Traffic Sign Detection and Recognition Under Complicated Lighting Conditions

Fig. 7. Detection results of proposed algorithm. (a) Detection results of GTSDB dataset; (b) detection results of CQD dataset; (c) detection results of CQN dataset

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Table 1. Detection performance of each algorithm

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Table 1. Detection performance of each algorithm

Dataset	Algorithm	TP	FN	FP	Precision /%	Recall rate /%
	Proposed algorithm	354	5	6	98.33	98.61
GTSDB	MCT-Adaboost	318	38	42	88.33	89.33
	HOG+SVM	349	5	7	98.03	98.58
	Proposed algorithm	583	14	10	98.31	97.65
CQD	MCT-Adaboost	519	71	85	85.93	87.97
	HOG+SVM	568	38	19	96.76	93.73
	Proposed algorithm	245	18	13	94.96	93.16
CQN	MCT-Adaboost	174	35	44	79.82	83.25
	HOG+SVM	206	24	33	86.19	89.57

Table 2. Recognition accuracy of each algorithm%
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Table 2. Recognition accuracy of each algorithm%
Dataset Accuracy
Proposed algorithm Multi-scale CNN^[15] Random forests^[16] LDA on HOG^[17]
GTSRB 98.94 98.31 97.20 95.68
CQD 98.37 97.88 95.24 93.07
CQN 96.61 91.75 84.37 67.33

Table 3. Running time cost of each algorithmms
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Table 3. Running time cost of each algorithmms
Algorithm Time cost
Proposed algorithm 81
MCT-Adaboost 394
HOG+SVM 267
Faster-RCNN 102

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Zhihua Qu, Yiming Shao, Tianmin Deng, Jie Zhu, Xiaohua Song. Traffic Sign Detection and Recognition Under Complicated Lighting Conditions[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231009

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