Vehicle detection using improved region convolution neural network for accident prevention in smart roads

Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Djenouri, Djamel; Line, Jerry Chun-Wei

Abstract

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data, which contains more than 200,000 vehicle images and 1,990,000 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances.

Citation

Djenouri, Y., Belhadi, A., Srivastava, G., Djenouri, D., & Line, J. C. (2022). Vehicle detection using improved region convolution neural network for accident prevention in smart roads. Pattern Recognition Letters, 158, 42-47. https://doi.org/10.1016/j.patrec.2022.04.012

• Journal Article Type: Article
• Acceptance Date: May 3, 2022
• Online Publication Date: Jun 22, 2022
• Publication Date: Apr 25, 2022
• Deposit Date: May 4, 2022
• Publicly Available Date: Jun 30, 2022
• Journal: Pattern Recognition Letters
• Print ISSN: 0167-8655
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 158
• Pages: 42-47
• DOI: https://doi.org/10.1016/j.patrec.2022.04.012
• Keywords: Deep learning, Vehicle detection, Region convolution neural network, Hyper-parameters optimization
• Public URL: https://uwe-repository.worktribe.com/output/9450889