Name
Abstract | ||
|---|---|---|
Vehicle type classification technology plays an important role in the intelligent transport systems nowadays. With the development of image processing, pattern recognition and deep learning, vehicle type classification technology based on deep learning has raised increasing concern. In the last few years, convolutional neural network, especially Faster Region-convolutional neural networks (Faster R-CNN) has shown great advantages in image classification and object detection. It has superiority to traditional machine learning methods by a large margin. In this paper, a vehicle type classification system based on deep learning is proposed. The system uses Faster R-CNN to solve the task. Experimental results show that the method is not only time-saving, but also has more robustness and higher accuracy. Aimed at cars and trucks, it reached 90.65 and 90.51% accuracy. At last, we test the system on an NVDIA Jetson TK1 board with 192 CUDA cores that is envisioned to be forerunner computational brain for computer vision, robotics and self-driving cars. Experimental results show that it costs around 0.354 s to detect an image and keeps high accurate rate with the network embedded on NVDIA Jetson TK1. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11554-017-0712-5 | Journal of Real-Time Image Processing |
Keywords | Field | DocType |
Convolutional neural network,Vehicle type classification,Deep learning,Intelligent transportation system,Object detection | Computer science,Convolutional neural network,Image processing,Real-time computing,Robustness (computer science),Artificial intelligence,Deep learning,Contextual image classification,Artificial neural network,Object detection,Computer vision,Intelligent transportation system,Machine learning | Journal |
Volume | Issue | ISSN |
16.0 | SP1.0 | 1861-8219 |
Citations | PageRank | References |
7 | 0.50 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinchen Wang | 1 | 7 | 1.51 |
| Weiwei Zhang | 2 | 7 | 0.50 |
| Xuncheng Wu | 3 | 7 | 0.50 |
| Lingyun Xiao | 4 | 66 | 3.31 |
| Yubin Qian | 5 | 7 | 0.50 |
| Zhi Fang | 6 | 7 | 0.50 |