ADVANCED OBJECT DETECTION IN MULTIBEAM FORWARD-LOOKING SONAR IMAGES USING LINEAR CROSS-ATTENTION TECHNIQUES

Sonar images object detection plays a crucial role in marine resource exploration and defense. Existing methods encounter challenges arising from non-rigid, deformed and low-resolution objects in sonar images, making them difficult to build a stable feature representation. In this paper, we present a novel object detection method based on linear cross-attention, aiming to construct a more robust feature representation tailored for sonar objects. Specifically, we introduce a novel feature fusion network designed to efficiently extract global object context. It helps to construct a more robust feature representation, effectively improving the model's ability to detect non-rigidly deformed and low-resolution objects. Moreover, we propose a linear attention mechanism to constitute the linear cross-attention module, leading to a significant reduction in computational load. Extensive experiments conducted on the public available dataset demonstrate the superiority of our approach. Our method surpasses a 1.4 mAP over the baseline, while requiring comparable parameters.