ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network
Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learnin...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/6972826 |
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| _version_ | 1832567427128360960 |
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| author | Jingming Xia Dawei Xuan Ling Tan Luping Xing |
| author_facet | Jingming Xia Dawei Xuan Ling Tan Luping Xing |
| author_sort | Jingming Xia |
| collection | DOAJ |
| description | Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then the characteristics extracted at the previous layer are shortcut to the next layer through four groups of residual modules. Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. In this paper, ResNet15 is used to train and test on the “WeatherDataset-4,” and desirable recognition results are obtained. The evaluation of a large number of experiments demonstrates that the proposed ResNet15 is superior to traditional network models such as ResNet50 in recognition accuracy, recognition speed, and model size. |
| format | Article |
| id | doaj-art-175ce6718f03418883040fc22b240da8 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-175ce6718f03418883040fc22b240da82025-02-03T01:01:31ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/69728266972826ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural NetworkJingming Xia0Dawei Xuan1Ling Tan2Luping Xing3School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, ChinaSevere weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then the characteristics extracted at the previous layer are shortcut to the next layer through four groups of residual modules. Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. In this paper, ResNet15 is used to train and test on the “WeatherDataset-4,” and desirable recognition results are obtained. The evaluation of a large number of experiments demonstrates that the proposed ResNet15 is superior to traditional network models such as ResNet50 in recognition accuracy, recognition speed, and model size.http://dx.doi.org/10.1155/2020/6972826 |
| spellingShingle | Jingming Xia Dawei Xuan Ling Tan Luping Xing ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network Advances in Meteorology |
| title | ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network |
| title_full | ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network |
| title_fullStr | ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network |
| title_full_unstemmed | ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network |
| title_short | ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network |
| title_sort | resnet15 weather recognition on traffic road with deep convolutional neural network |
| url | http://dx.doi.org/10.1155/2020/6972826 |
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