An Improved YOLOv8 OBB Model for Ship Detection through Stable Diffusion Data Augmentation

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 17; p. 5850
• Main Authors: Feng, Sang, Huang, Yi, Zhang, Ning
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09.09.2024
• Subjects: Accuracy; Algorithms; BiFPN structure; Boxes; Cameras; Computer vision; Datasets; Deep learning; Efficiency; EMA module; Methods; multi-scale detection; Neural networks; ship detection; stable diffusion; Telematics; Unmanned aerial vehicles; YOLOv8
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24175850

Unmanned aerial vehicles (UAVs) with cameras offer extensive monitoring capabilities and exceptional maneuverability, making them ideal for real-time ship detection and effective ship management. However, ship detection by camera-equipped UAVs faces challenges when it comes to multi-viewpoints, multi-scales, environmental variability, and dataset scarcity. To overcome these challenges, we proposed a data augmentation method based on stable diffusion to generate new images for expanding the dataset. Additionally, we improve the YOLOv8n OBB model by incorporating the BiFPN structure and EMA module, enhancing its ability to detect multi-viewpoint and multi-scale ship instances. Through multiple comparative experiments, we evaluated the effectiveness of our proposed data augmentation method and the improved model. The results indicated that our proposed data augmentation method is effective for low-volume datasets with complex object features. The YOLOv8n-BiFPN-EMA OBB model we proposed performed well in detecting multi-viewpoint and multi-scale ship instances, achieving the mAP (@0.5) of 92.3%, the mAP (@0.5:0.95) of 77.5%, a reduction of 0.8 million in model parameters, and a detection speed that satisfies real-time ship detection requirements.