WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture.

ABSTRACT: Smart farming (SF) applications rely on robust and accurate computer vision systems. An important computer vision task in agriculture is semantic segmentation, which aims to classify each pixel of an image and can be used for selective weed removal. State-of-the-art implementations use convolutional neural networks (CNN) that are trained on large image datasets. In agriculture, publicly available RGB image datasets are scarce and often lack detailed ground-truth information. In contrast to agriculture, other research areas feature RGB-D datasets that combine color (RGB) with additional distance (D) information. Such results show that including distance as an additional modality can improve model performance further. Therefore, we introduce WE3DS as the first RGB-D image dataset for multi-c

SUBMITTER: Kitzler F 

PROVIDER: S-EPMC10007111 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature