Vehicle Type Recognition Algorithm Based on Improved Network in Network
Vehicle type recognition algorithms are broadly used in intelligent transportation, but the accuracy of the algorithms cannot meet the requirements of production application. For the high efficiency of the multilayer perceptive layer of Network in Network (NIN), the nonlinear features of local recep...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6061939 |
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| _version_ | 1832559785999859712 |
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| author | Erxi Zhu Min Xu De Chang Pi |
| author_facet | Erxi Zhu Min Xu De Chang Pi |
| author_sort | Erxi Zhu |
| collection | DOAJ |
| description | Vehicle type recognition algorithms are broadly used in intelligent transportation, but the accuracy of the algorithms cannot meet the requirements of production application. For the high efficiency of the multilayer perceptive layer of Network in Network (NIN), the nonlinear features of local receptive field images can be extracted. Global average pooling (GAP) can avoid the network from overfitting, and small convolution kernel can decrease the dimensionality of the feature map, as well as downregulate the number of model training parameters. On that basis, the residual error is adopted to build a novel NIN model by altering the size and layout of the original convolution kernel of NIN. The feasibility of the algorithm is verified based on the Stanford Cars dataset. By properly setting weights and learning rates, the accuracy of the NIN model for vehicle type recognition reaches 97.2%. |
| format | Article |
| id | doaj-art-ae4a606a8e5b4f0abeec18e5e0e9a68a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ae4a606a8e5b4f0abeec18e5e0e9a68a2025-02-03T01:29:18ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/60619396061939Vehicle Type Recognition Algorithm Based on Improved Network in NetworkErxi Zhu0Min Xu1De Chang Pi2College of Internet of Things Engineering, Jiangsu Vocational College of Information Technology, Wuxi 214153, ChinaCollege of Electronic and Information Engineering, Jiangsu Vocational College of Information Technology, Wuxi 214153, ChinaCollege of Computer Science and Technology, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, ChinaVehicle type recognition algorithms are broadly used in intelligent transportation, but the accuracy of the algorithms cannot meet the requirements of production application. For the high efficiency of the multilayer perceptive layer of Network in Network (NIN), the nonlinear features of local receptive field images can be extracted. Global average pooling (GAP) can avoid the network from overfitting, and small convolution kernel can decrease the dimensionality of the feature map, as well as downregulate the number of model training parameters. On that basis, the residual error is adopted to build a novel NIN model by altering the size and layout of the original convolution kernel of NIN. The feasibility of the algorithm is verified based on the Stanford Cars dataset. By properly setting weights and learning rates, the accuracy of the NIN model for vehicle type recognition reaches 97.2%.http://dx.doi.org/10.1155/2021/6061939 |
| spellingShingle | Erxi Zhu Min Xu De Chang Pi Vehicle Type Recognition Algorithm Based on Improved Network in Network Complexity |
| title | Vehicle Type Recognition Algorithm Based on Improved Network in Network |
| title_full | Vehicle Type Recognition Algorithm Based on Improved Network in Network |
| title_fullStr | Vehicle Type Recognition Algorithm Based on Improved Network in Network |
| title_full_unstemmed | Vehicle Type Recognition Algorithm Based on Improved Network in Network |
| title_short | Vehicle Type Recognition Algorithm Based on Improved Network in Network |
| title_sort | vehicle type recognition algorithm based on improved network in network |
| url | http://dx.doi.org/10.1155/2021/6061939 |
| work_keys_str_mv | AT erxizhu vehicletyperecognitionalgorithmbasedonimprovednetworkinnetwork AT minxu vehicletyperecognitionalgorithmbasedonimprovednetworkinnetwork AT dechangpi vehicletyperecognitionalgorithmbasedonimprovednetworkinnetwork |