A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks
Vehicle type and brand information constitute a crucial element in intelligent transportation systems (ITSs). While numerous appearance-based classification methods have studied frontal view images of vehicles, the challenge of multi-pose and multi-angle vehicle distribution has largely been overloo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/23/9569 |
| _version_ | 1797399569364942848 |
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| author | Hongying Zhang Xusheng Li Huazhi Yuan Huagang Liang Yaru Wang Siyan Song |
| author_facet | Hongying Zhang Xusheng Li Huazhi Yuan Huagang Liang Yaru Wang Siyan Song |
| author_sort | Hongying Zhang |
| collection | DOAJ |
| description | Vehicle type and brand information constitute a crucial element in intelligent transportation systems (ITSs). While numerous appearance-based classification methods have studied frontal view images of vehicles, the challenge of multi-pose and multi-angle vehicle distribution has largely been overlooked. This paper proposes an appearance-based classification approach for multi-angle vehicle information recognition, addressing the aforementioned issues. By utilizing faster regional convolution neural networks, this method automatically captures crucial features for vehicle type and brand identification, departing from traditional handcrafted feature extraction techniques. To extract rich and discriminative vehicle information, ZFNet and VGG16 are employed. Vehicle feature maps are then imported into the region proposal network and classification location refinement network, with the former generating candidate regions potentially containing vehicle targets on the feature map. Subsequently, the latter network refines vehicle locations and classifies vehicle types. Additionally, a comprehensive vehicle dataset, Car5_48, is constructed to evaluate the performance of the proposed method, encompassing multi-angle images across five vehicle types and 48 vehicle brands. The experimental results on this public dataset demonstrate the effectiveness of the proposed approach in accurately classifying vehicle types and brands. |
| first_indexed | 2024-03-09T01:42:06Z |
| format | Article |
| id | doaj.art-768b35509d22439497ccf822a07fb23f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:42:06Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-768b35509d22439497ccf822a07fb23f2023-12-08T15:26:27ZengMDPI AGSensors1424-82202023-12-012323956910.3390/s23239569A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural NetworksHongying Zhang0Xusheng Li1Huazhi Yuan2Huagang Liang3Yaru Wang4Siyan Song5School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaVehicle type and brand information constitute a crucial element in intelligent transportation systems (ITSs). While numerous appearance-based classification methods have studied frontal view images of vehicles, the challenge of multi-pose and multi-angle vehicle distribution has largely been overlooked. This paper proposes an appearance-based classification approach for multi-angle vehicle information recognition, addressing the aforementioned issues. By utilizing faster regional convolution neural networks, this method automatically captures crucial features for vehicle type and brand identification, departing from traditional handcrafted feature extraction techniques. To extract rich and discriminative vehicle information, ZFNet and VGG16 are employed. Vehicle feature maps are then imported into the region proposal network and classification location refinement network, with the former generating candidate regions potentially containing vehicle targets on the feature map. Subsequently, the latter network refines vehicle locations and classifies vehicle types. Additionally, a comprehensive vehicle dataset, Car5_48, is constructed to evaluate the performance of the proposed method, encompassing multi-angle images across five vehicle types and 48 vehicle brands. The experimental results on this public dataset demonstrate the effectiveness of the proposed approach in accurately classifying vehicle types and brands.https://www.mdpi.com/1424-8220/23/23/9569vehicle typevehicle brandmulti-angle recognitionFaster R-CNNCar5_48 |
| spellingShingle | Hongying Zhang Xusheng Li Huazhi Yuan Huagang Liang Yaru Wang Siyan Song A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks Sensors vehicle type vehicle brand multi-angle recognition Faster R-CNN Car5_48 |
| title | A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks |
| title_full | A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks |
| title_fullStr | A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks |
| title_full_unstemmed | A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks |
| title_short | A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks |
| title_sort | multi angle appearance based approach for vehicle type and brand recognition utilizing faster regional convolution neural networks |
| topic | vehicle type vehicle brand multi-angle recognition Faster R-CNN Car5_48 |
| url | https://www.mdpi.com/1424-8220/23/23/9569 |
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