Dataset of bird's eye chilies farm for stereo image semantic segmentation

Abstract

This paper presents a dataset of bird's eye chilies in a single farm for semantic segmentation. The dataset is generated using two cameras that are aligned left and right forming a stereo-vision video capture. By analyzing the disparity between corresponding points in the left and right images, algorithms can calculate the relative distance of objects in the scene. This depth information is useful in various applications, including 3D reconstruction, object tracking, and autonomous navigation. The dataset consists of 1150 left and right compressed images extracted from ten sets of stereo videos taken at ten different locations within the chili farm from the same ages of the bird's eye chilies. Since the dataset is used for semantic segmentation, the ground truth images of manually semantic segmented images are also provided in the dataset. The dataset can be used for 2D and 3D semantic segmentation of the bird's eye view chili farm. Some of the object classes in this dataset are the sky, living things, plantation, flat, construction, nature, and misc.

Keywords

• Bird's eye chili farm
• Semantic segmentation
• Autonomous navigation