Etnubarhi, NahomPirehen, Hale FilizNagpal, SujalBayraktar, Irem2025-03-172025-03-172024979-835037943-3https://doi.org/10.1109/ASYU62119.2024.10757155https://hdl.handle.net/20.500.13099/1388IEEE SMC; IEEE Turkiye Section2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562Accurate detection of aircraft and ships from remote sensing images is crucial for various applications such as maritime surveillance, border security, and disaster management. In this study, we present a comprehensive evaluation of the performance of the YOLOv8 model in detecting aircraft and ships under diverse environmental conditions. Through extensive testing and analysis, we highlight the algorithm's capabilities and limitations in discerning objects amidst challenging backgrounds, varying lighting conditions, and complex contextual factors. Our results reveal notable successes in both aircraft and ship detection, alongside inherent challenges such as color similarity, shadow formations, and environmental turbulence. Additionally, performance metrics illustrate the model's superior efficacy in ship detection compared to aircraft detection, indicating the algorithm's adaptability to different object categories. This study contributes key insights into the capabilities of object detection algorithms in remote sensing applications, paving the way for further advancements in the field of aerial and maritime surveillance. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessdeep learningobject detectionsatellite imageryYOLOv8Conference Object10.1109/ASYU62119.2024.107571552-s2.0-85213397238N/A