Visual saliency prediction has been extensively studied in the context of standard dynamic range (SDR) display. Recently, high dynamic range (HDR) display has become popular, since HDR videos can provide the viewers more realistic visual experience than SDR ones. However, current studies on visual saliency of HDR videos, also called HDR saliency, are very few. Therefore, we establish an SDR-HDR Video pair Saliency Dataset (SDR-HDR-VSD) for saliency prediction on both SDR and HDR videos.

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.