تقرير
Bootstrapping Autonomous Driving Radars with Self-Supervised Learning
| العنوان: | Bootstrapping Autonomous Driving Radars with Self-Supervised Learning |
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| المؤلفون: | Hao, Yiduo, Madani, Sohrab, Guan, Junfeng, Alloulah, Mohammed, Gupta, Saurabh, Hassanieh, Haitham |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However, training radar models is hindered by the cost and difficulty of annotating large-scale radar data. To overcome this bottleneck, we propose a self-supervised learning framework to leverage the large amount of unlabeled radar data to pre-train radar-only embeddings for self-driving perception tasks. The proposed method combines radar-to-radar and radar-to-vision contrastive losses to learn a general representation from unlabeled radar heatmaps paired with their corresponding camera images. When used for downstream object detection, we demonstrate that the proposed self-supervision framework can improve the accuracy of state-of-the-art supervised baselines by $5.8\%$ in mAP. Code is available at \url{https://github.com/yiduohao/Radical}. Comment: 12 pages, 5 figures, to be published in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.04519 |
| رقم الأكسشن: | edsarx.2312.04519 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |