Summary of Enhancing Nighttime Vehicle Detection with Day-to-night Style Transfer and Labeling-free Augmentation, by Yunxiang Yang et al.
Enhancing Nighttime Vehicle Detection with Day-to-Night Style Transfer and Labeling-Free Augmentation
by Yunxiang Yang, Hao Zhen, Yongcan Huang, Jidong J. Yang
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework introduces a novel labeling-free data augmentation technique for daytime-to-nighttime image style transfer. The Efficient Attention Generative Adversarial Network is used to create realistic nighttime images, which are then fine-tuned with the YOLO11 model on an augmented dataset designed for rural nighttime environments. This results in significant improvements in vehicle detection and offers a scalable solution for enhancing AI-based detection systems in low-visibility environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds that existing object detection models struggle at night because they’re trained mainly on daytime images. Even humans have trouble labeling objects in low-light conditions, which is especially true when it comes to detecting vehicles on rural roads without streetlights or with headlights creating glare. To address this issue, the researchers developed a new way to enhance AI-based detection systems for nighttime environments. They used synthetic data and a special type of network called Efficient Attention Generative Adversarial Network to create realistic nighttime images. These images were then used to train a model to detect vehicles better at night. The results showed significant improvements in vehicle detection, making this approach useful for real-world applications. |
Keywords
» Artificial intelligence » Attention » Data augmentation » Generative adversarial network » Object detection » Style transfer » Synthetic data
