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. In addition, the YOLOv5 target detection network is improved by incorporating a rotation angle approach from the a priori frame design and the bounding box regression formulation to make it suitable for rotating frame-based detection scenarios. Finally, the weighted combination of the two difficult sample mining methods is used to improve the focal loss function, so as to improve the detection accuracy. The average accuracy of the test results of the improved algorithm on the DOTA data set is 77.01%, which is higher than the previous detection algorithm. Compared with the average detection accuracy of YOLOv5, the average detection accuracy is improved by 8.83%. The experimental results show that the algorithm has higher detection accuracy than other algorithms in remote sensing scenes.

The extraction of pavement damage information is one of the major difficulties in the application research of mobile laser scanning point cloud data. To address the problem of inaccurate detection results by using only relative distance to detect potholes, this paper proposes a novel pothole detection method that combines directed distance and skewed distribution. Firstly, the rapid positioning of the pothole is realized by the directed distance, which is calculated from the points and the local fitted plane. And monomerization and denoising of potential potholes are achieved by density clustering. Then, the new accurate plane is fitted by the surrounding pavement points of the potential pothole to obtain accurate directed distances. The negative skewed distribution of the directed distance histogram and the skewness coefficient are used for the accurate determination of the pothole. Finally, the three-dimensional geometric features of the pothole are extracted. Experiments were carried out on a road with poor road conditions. The experimental results validated the effectiveness and practicality of the proposed method. It can achieve automatic detection of potholes with different shapes and deformation degrees, and has effectively improved the efficiency of automatic road inspection.

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. In addition, we presented methods for assessing point cloud accuracy using these two forms of intensity information, along with their applicable scopes. By examining the percentage intensity, we analyzed the reflectance properties of the scanned object’s surface employing the Lambertian model. Our findings indicate that the Lambertian circle fitting radius is inversely correlated with the scanner’s ranging error at a constant scanning distance. Experimental outcomes substantiate that modifying the surface characteristics of the object enables the attainment of higher-precision point cloud data. By constructing a model associating the raw reflectance intensity with ranging errors, we developed a single-point error ellipsoid model to assess the accuracy of individual points within the point cloud. The experiments revealed that the ranging error model based on the raw intensity is solely applicable to point cloud data unaffected by specular reflectance properties. Moreover, the devised single-point error ellipsoid model accurately evaluates the measurement error of individual points. Both analytical methods can be utilized to evaluate the performance of the scanner as well as the accuracy of the acquired point cloud data, providing reliable data support for various high-precision applications.

Street trees providing ecological and cultural functions may block traffic signs from the driver’s view if they are not properly maintained. Aiming at the problems of low automation and poor refinement of the detection of tree areas blocking traffic signboards, and the inability to make pruning simulation and quantification of the occlude branches, in this paper, the detection of trees to be maintained and the simulation of branch pruning are studied based on mobile laser scanning point clouds. First, the tree areas that block the cantilever traffic signs from the driver’s view are automatically and accurately detected based on line of sight analysis. Then, via structured modeling, the pruning effect of trees under different pruning degrees is simulated in three dimensions, and four indicators (the number of pruning positions, the pruning branch volume, the pruning ratio of branch volume, and the pruning ratio of leaves) are proposed to quantify the pruning. The application in four different scenes proves the effectiveness of the proposed method. This research is helpful to automatically and accurately identify the street tree branches to be maintained, and provides an important reference for the formulation of branch pruning schemes that can comprehensively balance the pruning cost, tree ecological benefits and cultural benefits.

Accurate road surface and boundary information is vital for road damage detection, high-definition map construction, and traffic scene perception. To address the challenges posed by complex road scenes, irregular point cloud data, and class imbalance, this paper develops a novel dataset of mobile laser scanning (MLS) point clouds and proposes a deep learning-based method for road surface and road boundary segmentation. To handle the problem of large scenario spans and large data volumes, a time-based data partitioning strategy is adopted to preserve road geometry. And to tackle the class imbalance issue, a dynamic equilibrium sampling method is proposed. Moreover, the graph attention module is incorporated to enhance the model’s ability to capture structural features. Comprehensive experimental results demonstrated that the proposed model achieves outstanding performance in large-scale road scenes, with the overall accuracy exceeding 99%, mean accuracy surpassing 98%, and the mean intersection over union above 89%, significantly outperforming existing mainstream models. These results provide an efficient and reliable solution for the automated processing of MLS point clouds and key technical support for road extraction. Future research will focus on the study of cross-domain generalization capabilities across multiple scenarios.