DOI: 10.57760/sciencedb.13909
title: A Images Dataset of Rural Road for Instance Segmentation in Northern Xinjiang
title: 新疆北部乡村道路实例分割图像数据集

description: 　　This dataset is a collection of 1,285 valid high-definition images from 2021 to 2023, which were manually captured using a monocular sports video camera, GoPro HERO9 (pixels of 3840 × 2160), in the rural areas of northern Xinjiang, extracted by frame extraction technique, and pre-processed to obtain 1,285 valid high-definition images.　　The data collection locations were mainly selected from the countryside, fields, and urban and rural roads in northern Xinjiang. According to the main operating time and driving area of agricultural machines, the scenes are mainly asphalt, concrete, gravel, and dirt roads; the time is daytime and dusk; and the recognized target object categories are mainly: vehicles (agricultural machines, cars, trucks, tricycles, bicycles, etc.), pedestrians, livestock, and other static trees, traffic signs, street lamps, fences, and walls.　　By studying datasets such as Cityscapes, Mapillary Vistas, BDD100K, etc., and also based on the category objects in the images, this data is categorized into a total of 40 categories, which contain 20 instance categories. This data uses the polygonal annotation tool in the CVAT image annotation tool to manually annotate all the images at a detailed pixel level, in which a total of 10062 instance objects are annotated, which can meet the training needs of mainstream deep learning image segmentation models.　　This dataset includes three data files, of which: (1) images.zip is the original image file containing 1285 high-definition images of rural roads in the northern region of Xinjiang; (2) mask-images.zip is 1285 mask image files generated in one-to-one correspondence with the original images; (3) annotations.zip is the JSON file generated by annotation
description: 　　This dataset is a collection of 1,285 valid high-definition images from 2021 to 2023, which were manually captured using a monocular sports video camera, GoPro HERO9 (pixels of 3840 × 2160), in the rural areas of northern Xinjiang, extracted by frame extraction technique, and pre-processed to obtain 1,285 valid high-definition images.　　The data collection locations were mainly selected from the countryside, fields, and urban and rural roads in northern Xinjiang. According to the main operating time and driving area of agricultural machines, the scenes are mainly asphalt, concrete, gravel, and dirt roads; the time is daytime and dusk; and the recognized target object categories are mainly: vehicles (agricultural machines, cars, trucks, tricycles, bicycles, etc.), pedestrians, livestock, and other static trees, traffic signs, street lamps, fences, and walls.　　By studying datasets such as Cityscapes, Mapillary Vistas, BDD100K, etc., and also based on the category objects in the images, this data is categorized into a total of 40 categories, which contain 20 instance categories. This data uses the polygonal annotation tool in the CVAT image annotation tool to manually annotate all the images at a detailed pixel level, in which a total of 10062 instance objects are annotated, which can meet the training needs of mainstream deep learning image segmentation models.　　This dataset includes three data files, of which: (1) images.zip is the original image file containing 1285 high-definition images of rural roads in the northern region of Xinjiang; (2) mask-images.zip is 1285 mask image files generated in one-to-one correspondence with the original images; (3) annotations.zip is the JSON file generated by annotation