Real-Time Traffic Sign Detection Method Based on Improved Convolution Neural Network

Ying Tong; Huicheng Yang

doi:10.3788/LOP56.071003

Laser & Optoelectronics Progress, Volume. 56, Issue 7, 071003(2019)

Real-Time Traffic Sign Detection Method Based on Improved Convolution Neural Network

Ying Tong^* and Huicheng Yang

Author Affiliations

College of Electrical Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China

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Abstract Get PDF(in Chinese)

Figures & Tables(8)

Fig. 1. Flow chart of program

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Fig. 2. Structural diagram of NF R-CNN neural network

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Fig. 3. Three examples of traffic signs. (a) Mandatory; (b) prohibitory; (c) danger

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Fig. 4. Examples of test results by three models. (a) ZF; (b) VGG16; (c) NF R-CNN

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Fig. 5. P-R curves of three examples. (a) Mandatory; (b) prohibitory; (c) danger

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Table 1. Accuracy and recall rates at different time thresholds
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Table 1. Accuracy and recall rates at different time thresholds
Item Threshold Θ
0.1 0.2 0.4 0.5 0.6 0.7
Precision 0.7654 0.8826 0.9542 0.9688 09835 1
Recall 0.9542 0.9403 0.9125 0.8623 0.7854 0.6281

Table 2. Comparison of classification results by three models
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Table 2. Comparison of classification results by three models
Model Mandatory /% Prohibitory /% Danger /% Time /s
Model-ZF P 91.89 97.68 98.65 0.133
R 79.11 70.56 93.15
Model-VGG16 P 98.85 99.44 99.86 0.130
R 75.23 69.26 87.84
Model-NF R-CNN P 98.10 99.22 99.23 0.016
R 80.31 72.96 94.78

Table 3. AUC values and processing time
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Table 3. AUC values and processing time
Reference AUC value /% Time/s
Mandatory Prohibitory Danger
Ref. [8] 96.54 99.35 96.68 0.15
Ref. [9] 92.56 96.58 91.27 0.31
Ref. [10] 93.47 100 98.64 0.66
Ref. [11] 100 100 99.22 3.47
NF R-CNN 97.02 99.55 99.37 0.016

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Ying Tong, Huicheng Yang. Real-Time Traffic Sign Detection Method Based on Improved Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(7): 071003

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