MIP-Net: Multi-scale inverted pyramid network with local adaptive contrast for underwater object detection.

Abstract

Underwater objects often suffer from severe visual degradation, including strong background noise induced by water impurities, blurred boundaries caused by optical scattering, and small object suppression in complex environments. These factors collectively pose significant challenges to underwater object detection (UOD). To address these issues, we propose the Multi-Scale Inverted Pyramid Network (MIP-Net) tailored for UOD. Moving beyond standard feature fusion paradigms, MIP-Net introduces explicit conceptual shifts through two key components: the Local Adaptive Contrast module (LAC) and the Multi-Scale Inverted Feature Pyramid Network (MSIFPN). Unlike conventional global attention mechanisms, LAC selectively calibrates intra-feature contrast layer-by-layer, establishing a theoretical basis for dynamic feature modulation that prevents high-frequency detail dilution. Simultaneously, MSIFPN transcends traditional FPN architectures by coupling a dual-pyramid bidirectional flow with a strict mathematical foreground-background separation strategy, effectively isolating target semantics from ambiguous water impurities. Experiments on the official DUO benchmark demonstrate that MIP-Net achieves an AP of 70.1%, surpassing state-of-the-art single-stage, two-stage, and Transformer-based methods, while maintaining a highly competitive trade-off between computational efficiency (Params/FLOPs) and accuracy. Furthermore, evaluations on the terrestrial COCO dataset yield an AP of 45.6%, confirming the strong cross-domain generalization capability of our framework. The code is publicly available at: https://github.com/YitengGuo/MIP-Net.