Name
Title | ||
|---|---|---|
Deep learning-based algorithm for vehicle detection in intelligent transportation systems |
Abstract | ||
|---|---|---|
Object detection is an essential technology in the computer vision domain and plays a vital role in intelligent transportation. Intelligent vehicles utilize object detection on images for environment perception. This work develops a target detection algorithm based on deep learning technologies, particularly convolutional neural networks and neural network modeling. Building on the analysis of the traditional Haar-like vehicle recognition algorithm, a vehicle recognition algorithm based on a convolutional neural network with fused edge features (FE-CNN) is proposed. The experimental results demonstrate that FE-CNN improves the recognition precision and the model’s convergence speed through a simple and effective edge feature fusion method. In the experiment conducted using real traffic scene for vehicle recognition, the developed algorithm achieves a 99.82% recognition rate in efficient time, demonstrating the capability for real-time performance and accurate target detection. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11227-021-03712-9 | The Journal of Supercomputing |
Keywords | DocType | Volume |
Deep learning, Vehicle recognition, Convolution neural network, Edge features fusion | Journal | 77 |
Issue | ISSN | Citations |
10 | 0920-8542 | 0 |
PageRank | References | Authors |
0.34 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linrun Qiu | 1 | 0 | 0.34 |
| Dongbo Zhang | 2 | 1 | 0.68 |
| Yuan Tian | 3 | 270 | 21.90 |
| Najla Al-Nabhan | 4 | 19 | 6.49 |