Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery
Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and compreh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7662 |
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| author | Ruikang Luo Yaofeng Song Longfei Ye Rong Su |
| author_facet | Ruikang Luo Yaofeng Song Longfei Ye Rong Su |
| author_sort | Ruikang Luo |
| collection | DOAJ |
| description | Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog. |
| format | Article |
| id | doaj-art-f6f6723923b743a18ebf02e2ee4372d2 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-f6f6723923b743a18ebf02e2ee4372d22024-12-13T16:32:21ZengMDPI AGSensors1424-82202024-11-012423766210.3390/s24237662Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite ImageryRuikang Luo0Yaofeng Song1Longfei Ye2Rong Su3School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeSchool of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeAccurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and higher-dimensional information. In addition, due to the rapid development of deep neural network technology, image-based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve this problem. However, traditional purely convolution-based approaches have constraints on global information extraction, and complex environments such as bad weather seriously limit their recognition capability. To improve vehicle type classification capability under complex environments, this study proposes a novel Densely Connected Convolutional Transformer-in-Transformer Neural Network (Dense-TNT) framework for vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer-in-Transformer (TNT) layers. Vehicle data for three regions under four different weather conditions were deployed to evaluate the recognition capability. Our experimental findings validate the recognition ability of the proposed vehicle classification model, showing little decay even under heavy fog.https://www.mdpi.com/1424-8220/24/23/7662deep learningtransformerremote sensingvehicle classification |
| spellingShingle | Ruikang Luo Yaofeng Song Longfei Ye Rong Su Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery Sensors deep learning transformer remote sensing vehicle classification |
| title | Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery |
| title_full | Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery |
| title_fullStr | Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery |
| title_full_unstemmed | Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery |
| title_short | Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery |
| title_sort | dense tnt efficient vehicle type classification neural network using satellite imagery |
| topic | deep learning transformer remote sensing vehicle classification |
| url | https://www.mdpi.com/1424-8220/24/23/7662 |
| work_keys_str_mv | AT ruikangluo densetntefficientvehicletypeclassificationneuralnetworkusingsatelliteimagery AT yaofengsong densetntefficientvehicletypeclassificationneuralnetworkusingsatelliteimagery AT longfeiye densetntefficientvehicletypeclassificationneuralnetworkusingsatelliteimagery AT rongsu densetntefficientvehicletypeclassificationneuralnetworkusingsatelliteimagery |