Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model
Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surroundi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5531965 |
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| _version_ | 1832549768926068736 |
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| author | QiMing Wang JinMing Xu Tao Sun ZhiChao Lv GaoQiang Zong |
| author_facet | QiMing Wang JinMing Xu Tao Sun ZhiChao Lv GaoQiang Zong |
| author_sort | QiMing Wang |
| collection | DOAJ |
| description | Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition. |
| format | Article |
| id | doaj-art-0b67ffd9a1bb446eb7c866d5026e3ebd |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0b67ffd9a1bb446eb7c866d5026e3ebd2025-02-03T06:08:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55319655531965Research on Road Adhesion Condition Identification Based on an Improved ALexNet ModelQiMing Wang0JinMing Xu1Tao Sun2ZhiChao Lv3GaoQiang Zong4School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaAutomotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.http://dx.doi.org/10.1155/2021/5531965 |
| spellingShingle | QiMing Wang JinMing Xu Tao Sun ZhiChao Lv GaoQiang Zong Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model Journal of Advanced Transportation |
| title | Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model |
| title_full | Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model |
| title_fullStr | Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model |
| title_full_unstemmed | Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model |
| title_short | Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model |
| title_sort | research on road adhesion condition identification based on an improved alexnet model |
| url | http://dx.doi.org/10.1155/2021/5531965 |
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