Performance Improvement of Vehicle and Human Localization and Classification by YOLO Family Networks in Noisy UAV Images

Many important tasks in smart city development and management are solved by systems of monitoring and control installed on-board of unmanned aerial vehicles (UAVs). UAV sensors can be imperfect or they can operate in unfavorable conditions, which can then result in obtaining images or video sequences that are noisy. Noise can degrade the performance of methods of vehicle and human localization and classification. Therefore, specific techniques to improve performance have to be applied. In this paper, we consider YOLO family neural networks as tools for solving the aforementioned tasks. This family of networks is rapidly developing; however, the input data may still require pre-processing. One option is to apply denoising before object localization and classification. In addition, approaches based on augmentation and training can be used as well. We consider the performance of these approaches for various noise intensities. We identify the noise levels at which network performance starts to degrade and analyze possibilities of performance improvement for two filters–BM3D and DRUNet. Both improve such performance criteria as the F1 score, the Intersection over Union and the mean Average Precision. Datasets of urban areas are used in the network training and verification.