Vehicle classification algorithm based on binary proximity sensors and neural networks
To improve the classification accuracy, a new algorithm was developed with binary proximity magnetic sen- sors and back propagation neural networks. In this algorithm, use the low cost and high sensitive magnetic sensors to de- tect the magnetic field distortion when vehicle pass by them and estimat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2008-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74654691/ |
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| Summary: | To improve the classification accuracy, a new algorithm was developed with binary proximity magnetic sen- sors and back propagation neural networks. In this algorithm, use the low cost and high sensitive magnetic sensors to de- tect the magnetic field distortion when vehicle pass by them and estimate vehicle length with the geometrical characteris- tics of binary proximity networks, and finally classify vehicles via neural networks. The inputs to the neural networks in- clude the vehicle length, velocity and the sequence of features vector set, and the output is predefined vehicle types. Simulation and on-road experiment obtains high recognition rate of 93.61%. It verified that this algorithm enhances the vehicle classification with high accuracy and solid robustness. |
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| ISSN: | 1000-436X |