HazardNet：通过增强合成模型进行道路碎片检测
HazardNet: Road Debris Detection by Augmentation of Synthetic Models
摘要：
我们提出了一种算法来检测看不见的道路碎片，使用一个小的合成模型。道路碎片的早期检测对于安全的自动驾驶或辅助驾驶至关重要，然而，鲁棒的道路碎片检测模型的开发尚未得到广泛讨论。构建道路碎片检测器存在两个主要挑战：第一，道路碎片的数据汇集具有挑战性，因为道路上的危险物体在真实驾驶场景中很少遇到;第二，道路碎片的可变性很大，从非常小的砖块到大的倒下的树。为了克服这些挑战，我们提出了一种新的方法来少样本学习的道路碎片，使用语义增强和域随机化来增强真实的道路图像与合成模型。我们将问题域限制在道路上不常见的物体上，并允许深层神经网络HazardNet学习道路碎片的语义含义，以最终检测看不见的道路碎片。我们的研究结果表明，HazardNet仅在增强图像中的合成对象上进行训练时，就能够准确地检测到真实的道路碎片。
英文摘要：
We present an algorithm to detect unseen road debris using a small set of synthetic models. Early detection of road debris is critical for safe autonomous or assisted driving, yet the development of a robust road debris detection model has not been widely discussed. There are two main challenges to building a road debris detector: first, data collection of road debris is challenging since hazardous objects on the road are rare to encounter in real driving scenarios; second, the variability of road debris is broad, ranging from a very small brick to a large fallen tree. To overcome these challenges, we propose a novel approach to few-shot learning of road debris that uses semantic augmentation and domain randomization to augment real road images with synthetic models. We constrain the problem domain to uncommon objects on the road and allow the deep neural network, HazardNet, to learn the semantic meaning of road debris to eventually detect unseen road debris. Our results demonstrate that HazardNet is able to accurately detect real road debris when only trained on synthetic objects in augmented images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
