Accurate 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.
Assessing YOLOv8 Performance and Limitations in Aircraft and Ship Detection from Satellite Images
16.10.2024
407868 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Ship Detection Based on Improved YOLOv8 Algorithm
IEEE | 2024
|Ship target detection based on CBAM-YOLOv8
SPIE | 2024
|