Vehicle Detection at Night Based on Style Transfer Image Enhancement


Jianing Shen, Rong Li, Journal of Information Processing Systems Vol. 19, No. 5, pp. 663-672, Oct. 2023  

https://doi.org/10.3745/JIPS.02.0203
Keywords: BDD100K Dataset, cycleGAN, image enhancement, Style Transfer Model, Vehicle Detection at Night, YOLOv5s Network
Fulltext:

Abstract

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.


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Cite this article
[APA Style]
Shen, J. & Li, R. (2023). Vehicle Detection at Night Based on Style Transfer Image Enhancement. Journal of Information Processing Systems, 19(5), 663-672. DOI: 10.3745/JIPS.02.0203.

[IEEE Style]
J. Shen and R. Li, "Vehicle Detection at Night Based on Style Transfer Image Enhancement," Journal of Information Processing Systems, vol. 19, no. 5, pp. 663-672, 2023. DOI: 10.3745/JIPS.02.0203.

[ACM Style]
Jianing Shen and Rong Li. 2023. Vehicle Detection at Night Based on Style Transfer Image Enhancement. Journal of Information Processing Systems, 19, 5, (2023), 663-672. DOI: 10.3745/JIPS.02.0203.