| 제목 |
Improving Nighttime Curb Segmentation with Domain Translation |
| 분야 |
전기ㆍ전자ㆍ통신 |
| 언어 |
Korean |
| 저자 |
조경호(부산대학교), 안창선(부산대학교), 김주희(창원대학교) |
| Key Words |
Autonomous vehicle, Curb detection, Contrastive unpaired translation, Domain translation, Semantic segmentation |
| 초록 |
This research sought to address the crucial challenge of enhancing nighttime curb segmentation in autonomous driving systems. The study delved into the limitations of using cameras, especially in low-light conditions, and its effects on image semantic segmentation and curb detection. To overcome these challenges, a camera-based method was proposed, leveraging both synthetic day images and real images for domain transfer. The algorithm comprised a dedicated generator network for translation and a segmentation network trained with a conventional loss function. The results of the experiment demonstrated a 124 % improvement in curb segmentation performance by F1 score, along with a 24 % increase in precision, compared to the benchmark. The findings underscored the method’s significant potential in augmenting nighttime curb segmentation. This approach would be poised to contribute substantially to the development of safer autonomous vehicles that are equipped with heightened perception capabilities. |
| 원문(PDF) |
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