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- Title
Deep learning-based algorithm for vehicle detection in intelligent transportation systems.
- Authors
Qiu, Linrun; Zhang, Dongbo; Tian, Yuan; Al-Nabhan, Najla
- 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.
- Subjects
INTELLIGENT transportation systems; DEEP learning; ALGORITHMS; OBJECT recognition (Computer vision); CONVOLUTIONAL neural networks; ARTIFICIAL neural networks; COMPUTER vision
- Publication
Journal of Supercomputing, 2021, Vol 77, Issue 10, p11083
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-021-03712-9