SLENet: A Guidance-Enhanced Network for Underwater Camouflaged Object Detection
This addresses the underexplored task of underwater camouflaged object detection, which is important for marine ecology, but the approach is incremental as it builds on existing COD models with enhancements.
The paper tackles the problem of detecting camouflaged objects in underwater environments by introducing a new benchmark dataset (DeepCamo) and proposing SLENet, a framework that achieves superior performance over state-of-the-art methods on this dataset and other benchmarks.
Underwater Camouflaged Object Detection (UCOD) aims to identify objects that blend seamlessly into underwater environments. This task is critically important to marine ecology. However, it remains largely underexplored and accurate identification is severely hindered by optical distortions, water turbidity, and the complex traits of marine organisms. To address these challenges, we introduce the UCOD task and present DeepCamo, a benchmark dataset designed for this domain. We also propose Semantic Localization and Enhancement Network (SLENet), a novel framework for UCOD. We first benchmark state-of-the-art COD models on DeepCamo to reveal key issues, upon which SLENet is built. In particular, we incorporate Gamma-Asymmetric Enhancement (GAE) module and a Localization Guidance Branch (LGB) to enhance multi-scale feature representation while generating a location map enriched with global semantic information. This map guides the Multi-Scale Supervised Decoder (MSSD) to produce more accurate predictions. Experiments on our DeepCamo dataset and three benchmark COD datasets confirm SLENet's superior performance over SOTA methods, and underscore its high generality for the broader COD task.