This database contains Synthetic High-Voltage Power Line Insulator Images.

There are two sets of images: one for image segmentation and another for image classification.

The first set contains images with different types of materials and landscapes, including the following landscape types: Mountains, Forest, Desert, City, Stream, Plantation. Each of the above-mentioned landscape types consists of 2,627 images per insulator type, which can be Ceramic, Polymeric or made of Glass, with a total of 47,286 distinct images.

This a Lightning arrester point cloud dataset, using TXT documents to save, each file format is (8192, 7), 8192 means each file has 8192 points, where 1-3 columns are spatial dimensions, 4-6 columns are color information, and the last column is the label information of lightning arrester parts segmentation. It can be used to finished pointcloud segmention task.

While picking robots aim to address this, the complex growth environment poses challenges in identifying and locating fruits due to factors like light and leaf occlusion. This study focuses on designing a recognition and localization method tailored to the natural growth conditions of melons and fruits, aiming to provide precise positional information for effective harvesting. Leveraging GTR-Net and binocular stereo vision, the proposed technology integrates a lightweight backbone network with Ghost bottleneck and TCSPG modules.

The following are three publicly available datasets for experiments related to federated learning or machine learning.

Availability of Data and Materials: The datasets used to support the findings of this study are publicly available on Internet as follow:

MNIST: http://yann.lecun.com/exdb/mnist/;