A Cooperative Training Framework for Underwater Object Detection on a Clearer View

Underwater optical image object detection plays a crucial role in fields such as ocean exploration. However, constructing a comprehensive annotated dataset for training is challenging, especially when dealing with severely degraded underwater imagery. The sparsity of annotations can significantly reduce the performance of object detection algorithms. Existing methods designed for sparsely annotated object detection (SAOD) in terrestrial scenarios are not optimal for underwater conditions. To address these challenges, we propose a novel underwater cooperative training framework (CTF). Specifically, we propose a novel conjugate data generation module (CDGM) to tackle the issue of noise accumulation inherent in the existing data generation module, thereby greatly enhancing pseudo label generation. Furthermore, to mitigate the impacts of noisy pseudo labels, we present a pseudo label calibration strategy (PLCS) that manipulates the foreground confidence trend toward a low entropy distribution, effectively eliminating noisy pseudo labels. Finally, we propose a novel decoupled detection module to alleviate interference between position information and foreground confidence, further reducing noisy pseudo labels. Compared with methods tailored for terrestrial conditions with sparse annotations, our approach demonstrates superior performance in underwater scenarios. We conducted extensive experiments on various underwater datasets, including URPC2018, DUO, etc. The results show that our method outperforms the existing state-of-the-art by 2.0 mean average precision (mAP) on the URPC2018 and 0.9 mAP on the DUO datasets, while achieving state-of-the-art performance.