Understanding the factors that influence the quality of unmanned aerial vehicle (UAV)-based products is a scientifically ongoing and relevant topic. Our research focused on the impact of the interior orientation parameters (IOPs) on the positional accuracy of points in a calibration field, identified and measured in an orthophoto and a point cloud. We established a calibration field consisting of 20 materialized points and 10 detailed points measured with high accuracy. Surveying missions with a fixed-wing UAV were carried out in three series. Several image blocks that differed in flight direction (along, across), flight altitude (70 m, 120 m), and IOPs (known or unknown values in the image-block adjustment) were composed. The analysis of the various scenarios indicated that fixed IOPs, computed from a good geometric composition, can especially improve vertical accuracy in comparison with self-calibration; an image block composed from two perpendicular flight directions can yield better results than an image block composed from a single flight direction.
Measurement data obtained from Weigh-in-Motion systems support protection of road pavements from the adverse phenomenon of vehicle overloading. For this protection to be effective, WIM systems must be accurate and obtain a certificate of metrological legalization. Unfortunately there is no legal standard for accuracy assessment of Weigh-in-Motion (WIM) systems. Due to the international range of road transport, it is necessary to standardize methods and criteria applied for assessing such systems’ accuracy. In our paper we present two methods of determining accuracy of WIM systems. Both are based on the population of weighing errors determined experimentally during system testing. The first method is called a reliability characteristic and was developed by the authors. The second method is based on determining boundaries of the tolerance interval for weighing errors. Properties of both methods were assessed on the basis of simulation studies as well as experimental results obtained from a 16-sensor WIM system.