论文标题
不利天气中的视力：使用具有各种对象探测器的自行车探测器进行自主赛车的强大感知的增强
Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing
论文作者
论文摘要
在自主驾驶系统中，感知 - 对环境的特征和对象的识别至关重要。在自主赛车中，高速和较小的边缘需要快速，准确的检测系统。在比赛中，天气可能会突然改变，导致感知中的显着降解，导致动作无效。为了改善不利天气中的检测，基于深度学习的模型通常需要在这种情况下捕获的广泛数据集，其中收集是一个乏味，费力且昂贵的过程。但是，Cyclegan体系结构的最新发展允许在多种天气条件下综合高度逼真的场景。为此，我们介绍了一种在自主赛车中使用合成的不良条件数据集（使用CycleGAN生成）的方法，以分别在夜间条件和液滴的情况下，在五个最先进的探测器中的四个平均提高了42.7和4.4 MAP百分比。此外，我们对五个对象探测器进行了比较分析 - 确定在挑战性条件下自主赛车期间使用探测器和训练数据的最佳配对。
In an autonomous driving system, perception - identification of features and objects from the environment - is crucial. In autonomous racing, high speeds and small margins demand rapid and accurate detection systems. During the race, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions - the collection of which is a tedious, laborious, and costly process. However, recent developments in CycleGAN architectures allow the synthesis of highly realistic scenes in multiple weather conditions. To this end, we introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors by an average of 42.7 and 4.4 mAP percentage points in the presence of night-time conditions and droplets, respectively. Furthermore, we present a comparative analysis of five object detectors - identifying the optimal pairing of detector and training data for use during autonomous racing in challenging conditions.
