Efficient Underwater Object Detection With Enhanced Feature Extraction and Fusion

Underwater object detection is critical for applications, such as environmental monitoring, resource exploration, and the navigation of autonomous underwater vehicles. However, accurately detecting small objects in underwater environments remains challenging due to noisy imaging conditions, variable illumination, and complex backgrounds. To address these challenges, we propose the adaptive residual attention network (ARAN), an optimized deep learning framework designed to enhance the detection and precise identification of diminutive targets in complex aquatic settings. ARAN incorporates the proposed Fusion path aggregation network (PANet), which refines spatial features by effectively distinguishing objects from their backgrounds. The framework integrates three novel modules: first, multiscale feature attention, which enhances low-level feature extraction; second, high–low feature residual learning, which rearranges channel and batch dimensions to capture pixel-level relationships through cross-dimensional interactions; and third, multilevel feature dynamic aggregation, which dynamically adjusts fusion weights to facilitate progressive multilevel feature fusion and mitigate conflicts in multiscale integration, ensuring that small objects are not overshadowed. Extensive experiments on four benchmark datasets demonstrate that ARAN significantly outperforms mainstream models, achieving state-of-the-art performance. Notably, on the CSIRO dataset, ARAN attains a mean average precision at 50% of 98%, precision of 94.7%, F2-score of 94.6%, and recall of 94.7%. These results confirm our model's superior accuracy, robustness, and efficiency in underwater object detection, highlighting its potential for practical deployment in challenging aquatic environments. We will release the code on GitHub upon acceptance of the article.