Nighttime Data Augmentation Using GAN for Improving Blind-Spot Detectionopen access
- Authors
- Lee, Hongjun; Ra, Moonsoo; Kim, Whoi-Yul
- Issue Date
- Mar-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Gallium nitride; Training; Cameras; Generative adversarial networks; Task analysis; Databases; Accidents; Data augmentation; domain adaptation; generative adversarial networks; blind-spot detection
- Citation
- IEEE ACCESS, v.8, pp.48049 - 48059
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 48049
- End Page
- 48059
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/10577
- DOI
- 10.1109/ACCESS.2020.2979239
- Abstract
- Camera-based blind-spot detection systems improve the shortcomings of radar-based systems for accurately detecting the position of a vehicle. However, as with many camera-based applications, the detection performance is insufficient in a low-illumination environment such as at night. This problem can be solved with augmented nighttime images in the training data but acquiring them and annotating the additional images are cumbersome tasks. Therefore, we propose a framework that converts daytime images into synthetic nighttime images using a generative adversarial network and that augments the synthetic images for the training process of the vehicle detector. A public dataset comprising different viewpoints of target images was used to easily obtain the images required for training the generative adversarial network. Experiments on a real nighttime dataset demonstrate that the proposed framework improved the detection performance considerably in comparison with using daytime images only.
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