A Video Dataset for Visual Perception and Autonomous Navigation in Unstructured Environments. Website: http://rugd.vision/
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Risk-Aware Planning is a dataset that contains the overhead images and their semantic segmentation captured by a drone from the CityEnviron environment in AirSim simulator.
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The SBCoseg dataset includes 889 groups of images and each group consists of 18 images with a common object, leading to 16002 images in total. The whole dataset is divided into five subsets: with ECFB, with TR, with MH, with SD, and Normal (normal data). The five subsets contain 193, 251, 82, 83, and 280 image groups, respectively. Each original image is in JPG format with a pixel size of 360 ×360, and each ground-truth image is in PNG format.
Test dataset for Semantic Segmentation. The datasets includes 500 RGB - images with the relative single-channel binary masks. Images are taken from the vineyards in Grugliasco - Turin - Piedmont Region -Italy
The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent
The dataset contains procedurally generated images of transparent vessels containing liquid and objects . The data for each image includes segmentation maps, 3d depth maps, and normal maps of of the liquid or object inside the transparent vessel, and the vessel. In addition, the properties of the materials inside the containers are given(color/transparency/roughness/metalness). In addition, a natural image benchmark for the 3d/depth estimation of objects inside transparent containers is supplied. 3d models of the objects (GTLF) are also supplied.
This is the first general Underwater Image Instance Segmentation (UIIS) dataset containing 4,628 images for 7 categories with pixel-level annotations for underwater instance segmentation task
The semantic segmentation of clothes is a challenging task due to the wide variety of clothing styles, layers and shapes. The UTFPR-SBD3 contains 4,500 images manually annotated at pixel level in 18 classes plus background. To ensure the high quality of the dataset, all images were manually annotated at the pixel level using JS Segment Annotator, 2 a free web-based image annotation tool. The raw images were carefully selected to avoid, as far as possible, classes with low number of instances.
This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (Broken-B, Discolored-D, Silkcut-S, and Pure-P) 17802 images are labeled by the experts at the AdTech Corp. and 26K images were unlabeled out of which 9k images were labeled using the Active Learning (BatchBALD)
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Lemon dataset has been prepared to investigate the possibilities to tackle the issue of fruit quality control. It contains 2690 annotated images (1056 x 1056 pixels). Raw lemon images have been captured using the procedure described in the following blogpost and manually annotated using CVAT.
Multi-grained Vehicle Parsing (MVP) is a large-scale dataset for semantic analysis of vehicles in the wild, which has several featured properties. 1. The MVP contains 24,000 vehicle images captured in read-world surveillance scenes, which makes it more scalable for real applications. 2. For different requirements, we annotate the vehicle images with pixel-level part masks in two granularities, i.e., the coarse annotations of ten classes and the fine annotations of 59 classes. The former can be applied to object-level applications such as vehicle Re-Id, fine-grained classification, and pose estimation, while the latter can be explored for high-quality image generation and content manipulation. 3. The images reflect the complexity of real surveillance scenes, such as different viewpoints, illumination conditions, backgrounds, and etc. In addition, the vehicles have diverse countries, types, brands, models, and colors, which makes the dataset more diverse and challenging.
UAS-based Multispectral othomosaics of vineyards from central Portugal