Feature-adaptive FPN with multiscale context integration for underwater object detection

Abstract

Underwater object detection is vital for diverse applications, from studies in marine biology to underwater robotics. However, underwater environments pose unique challenges, including reduced visibility due to color distortion, light attenuation, and complex backgrounds. Traditional computer vision methods have limitations, prompting the implementation of deep learning, for underwater object detection. Despite progress, challenges persist, such as visual degradation, scale variations, diverse marine species, and complex backgrounds. To address these issues, we propose Feature-Adaptive FPN with Multiscale Context Integration (FA-FPN-MCI), a novel deep-learning algorithm aimed at enhancing both detection and domain generalization performance. We integrate the Style Normalization and Restitution (SNR) module for domain generalization, Receptive Field Blocks (RFBs) for fine-grained detail capture, and a twin-branch Global Context Module (TBGCM) for multiscale context information. We enhance lateral connections within the Feature Pyramid Network (FPN) with deformable convolution. Experimental outcome reveal that the proposed method attains mean average precision of 84.2%. Additionally, other performance metrics were evaluated, and outperforming all other methods used for comparison.