Underwater Side-Scan Sonar Target Detection: An Enhanced YOLOv11 Framework Integrating Attention Mechanisms and a Bi-Directional Feature Pyramid Network

Underwater target detection is pivotal for marine exploration, yet it faces significant challenges because of the inherent complex underwater environment. Sonar images are generally degraded by noise, exhibit low resolution, and lack prominent target features, making the extraction of useful feature information from blurred and complex backgrounds particularly challenging. These limitations hinder highly accurate autonomous target detection in sonar imagery. To address these issues, this paper proposes the ABFP-YOLO model, which was designed to enhance the accuracy of underwater target detection. Specifically, the bi-directional feature pyramid network (BiFPN) structure is integrated into the model to efficiently fuse the features of different scales, significantly improving the capability of the network to recognize targets of varying scales, especially small targets in complex scenarios. Additionally, an attention module is incorporated to enhance feature extraction from blurred images, thereby boosting the detection accuracy of the model. To validate the proposed model’s effectiveness, extensive comparative and ablation experiments were conducted on two datasets. The experimental results demonstrate that the ABFP-YOLO model achieves mean average precision (mAP0.5) scores of 0.988 and 0.866, indicating its superior performance in target detection tasks within complex underwater environments.