Semantic Segmentation and Inpainting of Dust with the S-Dust Dataset

Abstract

In agriculture the performance of camera-based vision systems is strongly affected by environmental factors, such as rain, fog, and dust. This paper presents the first open dataset for training and evaluating image processing algorithms that remove swirling dust from images. It includes images from agricultural fields during tillage, which raises dust that is manually labeled in the images. However, dust is not a classical object that can be delineated based on edges. Moreover, the dust density is not uniformly distributed but varies and is concentrated in the area behind the implement. Therefore, different segmentation approaches were investigated. First, traditional and deep-learning methods for dust segmentation in images were compared. For the neural network, a Unet with a pretrained VGG encoder was chosen. The results show that the network cannot distinguish from the background in areas with low dust density. In contrast, the traditional segmentation method based on dark channel prior (DCP) outperforms it. Subsequently, various inpainting methods to remove dust were investigated. One observation was that dust removal from images cannot be solved with current inpainting methods. The dataset is available at *.*www.agriscapes-dataset.com