This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures’ presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified.

Regular power line inspections are essential to ensure the reliability of electricity supply. The inspections of overground power submission lines include corridor clearance monitoring and fault identification. The power lines corridor is a three-dimensional space around power cables defined by a set distance. Any obstacles breaching this space should be detected, as they potentially threaten the safety of the infrastructure. Corridor clearance monitoring is usually performed either by a labor-intensive total station survey (TS), terrestrial laser scanning (TLS), or expensive airborne laser scanning (ALS) from a plane or a helicopter. This paper proposes a method that uses unmanned aerial vehicle (UAV) images to monitor corridor clearance. To maintain the adequate accuracy of the relative position of wires in regard to surrounding obstacles, the same data were used both to reconstruct a point cloud representation of a digital surface model (DSM) and a 3D power line. The proposed algorithm detects power lines in a series of images using decorrelation stretch for initial image processing, the modified Prewitt filter for edge enhancement, random sample consensus (RANSAC) with additional parameters for line fitting, and epipolar geometry for 3D reconstruction. DSM points intruding into the corridor are then detected by calculating the spatial distance between a reconstructed power line and the DSM point cloud representation. Problematic objects are localized by segmenting points into voxels and then subsequent clusterization. The processing results were compared to the results of two verification methods—TS and TLS. The comparison results show that the proposed method can be used to survey power lines with an accuracy consistent with that of classical measurements.

Recently, the term smartphone photogrammetry gained popularity. This suggests that photogrammetry may become a simple measurement tool by virtually every smartphone user. The research was undertaken to clarify whether it is appropriate to use the Structure from Motion—Multi Stereo View (SfM-MVS) procedure with self-calibration as it is done in Uncrewed Aerial Vehicle photogrammetry. First, the geometric stability of smartphone cameras was tested. Fourteen smartphones were calibrated on the checkerboard test field. The process was repeated multiple times. These observations were found: (1) most smartphone cameras have lower stability of the internal orientation parameters than a Digital Single-Lens Reflex (DSLR) camera, and (2) the principal distance and position of the principal point are constantly changing. Then, based on images from two selected smartphones, 3D models of a small sculpture were developed. The SfM-MVS method was used, with self-calibration and pre-calibration variants. By comparing the resultant models with the reference DSLR-created model it was shown that introducing calibration obtained in the test field instead of self-calibration improves the geometry of 3D models. In particular, deformations of local concavities and convexities decreased. In conclusion, there is real potential in smartphone photogrammetry, but it also has its limits.

This paper details the development of a camera calibration method purpose-built for use in photogrammetric survey production. The calibration test field was established in a hangar, where marker coordinates were measured using a high-precision survey methodology guaranteeing very high accuracy. An analytical model for bundle adjustment was developed that does not directly use the coordinates of field calibration markers but integrates bundle adjustment and survey observations into a single process. This solution, as well as a classical calibration method, were implemented in a custom software, for which the C++ source code repository is provided. The method was tested using three industrial cameras. The comparison was drawn towards a baseline method, OpenCV implementation. The results point to the advantages of using the proposed approach utilizing extended bundle adjustment.

The paper presents a pumpkin yield estimation method using images acquired by a UAV. The processing pipeline is fully automated. It consists of orthomosaic generation, a color model collection using a random subset of the data, color segmentation, and finally counting of pumpkin blobs together with assessing the number of pumpkins in each blob. The algorithm was validated by a manual check of 5% of each tested dataset. The precision value ranges between 0.959 and 0.996, recall between 0.971 and 0.987, and F1 score falls between 0.971 and 0.988. This proves the very high efficiency of the processing workflow and its potential value to farmers.

The problem of brightness differences between images of the same scene is important to the field of unmanned aerial vehicle (UAV) photogrammetry and affects both the aesthetics and the interpretation of the final product. This problem can be caused by changes in the positions of the camera or sun, as well as weather conditions. This article deals with the problem of varied image brightness caused by the latter. Relative radiometric normalisation (RRN) of acquired RGB imagery is used to diminish the effects of this phenomenon and improve the visual quality of the resultant product. The presented algorithm considers the specificity of UAV‐acquired data. It utilises image positions and their relationships to group similar images, choose references and perform RRN via histogram matching. The final method is robust and fully automatic. Validation performed on two independent datasets confirms its effectiveness both qualitatively (improving the appearance of the orthomosaic) and quantitatively.