Impact of distortions in UAV images on quality and accuracy of object localization

The localization and classification of objects of different types in images is an important and actively researched topic because the designed methods and tools are exploited in a wide variety of fields, including remote sensing, security systems, and medical diagnostics. Imaging systems installed on-board unmanned aerial vehicles (UAVs) and drones have become popular recently, and they are potentially beneficial for numerous applications like mine detection, traffic control, and crowd control. Images acquired by such systems may suffer from low quality because of the use of rather cheap cameras and the necessity to transfer obtained data via communication lines with limited bandwidth, employing lossy compression. These factors can influence the quality and accuracy of object localization, which is typically negatively performed by trained neural networks. However, the intensity of the noise and distortions that can be considered acceptable, i.e. such that they do not lead to radical reduction of the performance characteristics are unclear. Given this, it is reasonable to investigate the impact of these effects on the quality of object localization and classification using a reliable data size and various noise/distortion intensities. Therefore, the research subject of this paper is the performance of object localization and classification methods for color images acquired by UAV-installed sensors. The primary focus is on the dependence of localization and classification metrics on the noise intensity, where the simulated noise mimics not only noise but also distortions due to lossy compression by modern coders. The aim of this work is to obtain adequate statistics and analyze them to build dependencies of the metrics on the intensity of distortions. The objective is to obtain conditions for which the effects of noise and distortions can be considered negligible or acceptable in practice. The second objective is to analyze the sensitivity of several modern neural network models to noise/distortions. The result is a statistical assessment of the dependence of model performance on input data quality. The conclusions are based on the statistics characterizing the model performance for the noise/distortion intensity interval. The conclusions allow the selection of the best (most robust) neural networks and the establishment of appropriate performance conditions.