Underwater Salient Object Detection via Dual-Stage Self-Paced Learning and Depth Emphasis

Salient object detection of underwater scenes (USOD) poses greater challenges than that of traditional terrestrial scenes due to the presence of diverse and complex underwater image degradation. Current deep learning-based USOD methods generally treat all samples equally while failing to account for the varying difficulty levels of different training samples, thus leading to a limited performance. To tackle this challenge, this paper introduces a novel deep USOD method which benefits from iterative Dual-stage Self-paced Learning (DSPL) and Salient Object Depth Emphasis (SODE). Specifically, a DSPL strategy, which enforces the network to only focus on simpler samples in the first stage and then shifts attention to more challenging samples in the second stage, is devised to imitate the learning process of humans. The whole network is iteratively trained with the DSPL strategy and thus gradually adapted to various underwater scenes with different difficulty levels. Additionally, the proposed method involves an SODE module, which adaptively enhances depth information to effectively locate salient objects, addressing the issue of unreliable depth data caused by underwater image quality degradation. Experimental results on two benchmark datasets demonstrate the superior performance of the proposed method against state-of-the-art methods. The source code of our method will be made available at https://github.com/NIT-JJH/SPDE.