Dual Attention Network for the Classification of Road Surface Conditions Based on EfficientNet

YANG Wei; HUANG Lihong; QU Xiaolei

doi:10.3901/JME.2022.24.211

Volume 58 Issue 24

Dec 2022

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JOURNAL OF MECHANICAL ENGINEERING > 2022 > 58(24): 211-222.

YANG Wei, HUANG Lihong, QU Xiaolei. Dual Attention Network for the Classification of Road Surface Conditions Based on EfficientNet[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 58(24): 211-222. doi: 10.3901/JME.2022.24.211

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YANG Wei, HUANG Lihong, QU Xiaolei. Dual Attention Network for the Classification of Road Surface Conditions Based on EfficientNet[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 58(24): 211-222. doi: 10.3901/JME.2022.24.211

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Dual Attention Network for the Classification of Road Surface Conditions Based on EfficientNet

doi: 10.3901/JME.2022.24.211

YANG Wei^1
,,
HUANG Lihong¹,
QU Xiaolei^{2,*
,
,}

1.
School of Automobile, Chang'an University, Xi'an 710064
2.
School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191

Received Date: 01 Feb 2022
Rev Recd Date: 05 Jul 2022

Available Online: 07 Mar 2024

Issue Publish Date: 20 Dec 2022

Abstract

Abstract

Given the drawback of information loss of high-level features caused by convolution operations when the existing EfficientNet is applied to classify asphaltroad surfaces, a novel dual attention mechanism combined two types of attention modules, named channel attention module and position attention module respectively, is introduced to the existing EfficientNet, and the dual attention network based on EfficientNet (DAEfficientNet) is proposed using sigmoid linear unit (SiLU) activation functions and a cosine learning rate decay technique. First, a dataset including 5, 938 images of five types of asphalt road surfaces under various weather conditions is constructed. The snow image samples of asphalt road surfaces are from the open-source dataset named Canadian adverse driving conditions dataset (CADCD). Second, the proposed model is trained and the image classification results are produced. Finally, the accuracy, precision, recall, F1 score, and specificity of the analyzed models are calculated to compare the classification performance between the proposed model and the others previous convolutional neural network models. The experimental results show that the proposed method outperforms the others completing methods and achieves higher accuracy and stronger robustness in the task of classification of the five types of road surface images.
- convolutional neural network,
- dual attention mechanism,
- asphalt road,
- road classification

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References(41)

References

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