Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture.

翻译：农业景观中的粉尘是一个重大挑战，会影响例如自主农业机械的环境感知能力。图像增强算法可用于减少粉尘。然而，这些算法需要同一环境中有尘和无尘图像进行验证。实际上，据我们所知，目前尚无数据集解决这一问题。因此，我们提出了agriscapes RB-Dust数据集，其命名源于其基于参考的除尘目的。在耕作过程中无法从驾驶室内拍摄照片，因为这会导致图像偏移。为此，我们搭建了一个固定拍摄装置，使其能够在靠近过往拖拉机的静止位置拍摄图像。测试装置基于一个半侧门架，拖拉机可从中穿过。田间试验在德国巴伐利亚的一个农场进行，测试时间为耕作期间。试验过程中，我们控制了土壤湿度和风速等其他参数，因为这些参数显著影响粉尘的生成。我们使用对比度增强和图像去雾算法验证了数据集，并分析了从移动拖拉机拍摄的图像的泛化能力。最后，我们基于高层视觉任务（如人体分类）展示了除尘的应用效果。我们的实证研究证实了RB-Dust在农业视觉除尘中的有效性。