Automatic Target Recognition using Unmanned Aerial Vehicle Images with Proposed YOLOv8-SR and Enhanced Deep Super-Resolution Network - Mishra, Gangeshwar; Tanwar, Rohit; Gupta, Prinima (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2025)
Modern surveillance necessitates the use of automatic target recognition (ATR) to identify targets or objects quickly and accurately for multiclass classification in unmanned aerial vehicles (UAVs) such as pedestrians, people, bicycles, cars, vans, trucks, tricycles, buses, and motors. The inadequate recognition rate in target detection for UAVs could be due to the fundamental issues provided by the poor resolution of photos recorded from the distinct perspective of the UAVs. The VisDrone dataset used for image analysis consists of a total of 10,209 UAV photos. This research work presents a comprehensive framework specifically for multiclass target classification using VisDrone UAV imagery. The YOLOv8-SR, which stands for