A Method for Point Cloud Accuracy Analysis Based on Intensity Information.

Three-dimensional laser scanning has emerged as a prevalent measurement method in numerous high-precision applications, and the precision of the obtained data is closely related to the intensity information. Comprehending the association between intensity and point cloud accuracy facilitates scanner performance assessment, optimization of data acquisition strategies, and evaluation of point cloud precision, thereby ensuring data reliability for high-precision applications. In this study, we investigated the correlation between point cloud accuracy and two distinct types of intensity information.

R-YOLO: A YOLO-Based Method for Arbitrary-Oriented Target Detection in High-Resolution Remote Sensing Images.

In view of the existence of remote sensing images with large variations in spatial resolution, small and dense objects, and the inability to determine the direction of motion, all these components make object detection from remote sensing images very challenging. In this paper, we propose a single-stage detection network based on YOLOv5. This method introduces the MS Transformer module at the end of the feature extraction network of the original network to enhance the feature extraction capability of the network model and integrates the Convolutional Block Attention Model (CBAM) to find the attention area in dense scenes.