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Erschienen in: The Journal of Supercomputing 10/2021

18.03.2021

Deep learning-based algorithm for vehicle detection in intelligent transportation systems

verfasst von: Linrun Qiu, Dongbo Zhang, Yuan Tian, Najla Al-Nabhan

Erschienen in: The Journal of Supercomputing | Ausgabe 10/2021

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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.

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Metadaten
Titel
Deep learning-based algorithm for vehicle detection in intelligent transportation systems
verfasst von
Linrun Qiu
Dongbo Zhang
Yuan Tian
Najla Al-Nabhan
Publikationsdatum
18.03.2021
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 10/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-021-03712-9

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