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Featured Application: This work can be potentially applied to the recognition of traffic signs in intelligent driving vehicles and automatic inspection of power systems. Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP@0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network's rank-1 performance by 3.86%. [ABSTRACT FROM AUTHOR]

In order to implement the spirit of General Secretary Xi Jinping's important speech on strengthening the comprehensive control of desertification and promoting key ecological engineering projects such as the "Three-North Shelterbelt Program", this study is designated to make concrete efforts to win the battle against desertification on the edge of Hexi Corridor--Taklimakan Desert. The study deeply explores the situation of wind-blown sand flux and prevention tasks in the area. Based on the characteristics of desertified land and wind-blown sand activities on the edge of Hexi Corridor--Taklimakan Desert, the goal is set to effectively prevent the spread of sand and control dust sources. The core focus is on wind prevention, sand resistance, and dust control. The strategic thinking has been formulated for the counterattack battle on the edge of Hexi Corridor--Taklimakan Desert. By systematically analyzing the source areas and paths of "wind, sand, and dust" and targeting the key zones of the desert edge and areas requiring prevention, the key areas and tasks are proposed for desertification control on the edge of the Hexi Corridor--Taklimakan Desert, so as to provide scientific and technological support for winning the battle against desertification on the edge of Hexi Corridor--Taklimakan Desert. [ABSTRACT FROM AUTHOR]

The utility pole inclination angle is an important parameter for determining pole health conditions. Without depth information, the angle cannot be estimated from a 2D image, and without large labeled reference pole data, it is time consuming to locate the pole in the 3D point cloud. Therefore, this paper proposes a method that processes the pole data from the 2D image and 3D point cloud to automatically measure the pole inclination angle. Firstly, the mask of the pole skeleton is obtained from an improved Mask R-CNN. Secondly, the pole point cloud is extracted from a PointNet that deals with the generated frustum from the pole skeleton mask and depth map fusion. Finally, the angle is calculated by fitting the central axis of the pole cloud data. ApolloSpace open dataset and laboratory data are used for evaluation. The experimental results show that the AP75 of improved Mask R-CNN is 58.15%, the accuracy of PointNet is 92.4%, the average error of pole inclination is 0.66°, and the variance is 0.12°. It is proved that the method can effectively realize the automatic measurement of pole inclination. [ABSTRACT FROM AUTHOR]