YOLOv8-FDF: A Small Target Detection Algorithm in Complex Scenes

Abstract

Synthetic Aperture Radar (SAR) finds widespread applications in environmental monitoring, disaster management, ship surveillance, and military intelligence. However, existing target detection methods are ineffective in SAR scenes due to the intricate background environments, small target displays, and irregular appearances. To address these challenges, this thesis introduces a target detection model named YOLOv8-FDF, tailored for SAR scenes based on the YOLOv8 architecture. The model effectively incorporates the FADC module to distinguish targets from complex backgrounds and integrates a deformable feature adaptive mechanism to focus on irregular targets. Additionally, this thesis devised a specialized detection head designed to identify small targets in SAR-wide scenes, thereby improving the effectiveness of detecting such targets. The proposed YOLOv8-FDF model is evaluated on the HRSID dataset. Experiment results show a 3.6% improvement in Map75 on both the training and test sets. Furthermore, under the COCO standard, the model achieves improvements of 4.1%, 2.9%, and 5.5% on AP, AP50, and AP75, along with 6.8%, 1.2%, and 1.2% improvements on small, medium, and large-sized ship detection. An accuracy enhancement of 6.8%, 1.0%, and 14.9% is achieved. These experimental findings validate the efficacy of the proposed YOLOv8-FDF model in SAR scenarios.

Keywords

• Small targets
• YOLOv8
• deep learning
• SAR target detection
• complex environment