APGNet: Adaptive Prior-Guided for Underwater Camouflaged Object Detection

2025-10-13

AI Summary

Addressing the dual challenges of image degradation (e.g., low contrast, chromatic distortion) and biological natural camouflage in underwater camouflaged object detection, this paper proposes the Adaptive Prior-Guided Network (APGN). Methodologically, APGN incorporates a multi-scale retinal enhancement and color restoration module to improve input quality; an extended receptive field module and a multi-scale progressive decoder to model long-range contextual dependencies; and a novel hierarchical adaptive prior-guided mechanism that jointly integrates positional and boundary priors with spatial attention for coarse localization, while leveraging deformable convolution to refine contour details. Evaluated on two public MAS benchmarks, APGN consistently outperforms 15 state-of-the-art methods, achieving substantial gains in detection accuracy and robustness. This work establishes a new paradigm for underwater ecological monitoring and resource exploration.

Technology Category

• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Detecting camouflaged objects in underwater environments is crucial for marine ecological research and resource exploration. However, existing methods face two key challenges: underwater image degradation, including low contrast and color distortion, and the natural camouflage of marine organisms. Traditional image enhancement techniques struggle to restore critical features in degraded images, while camouflaged object detection (COD) methods developed for terrestrial scenes often fail to adapt to underwater environments due to the lack of consideration for underwater optical characteristics. To address these issues, we propose APGNet, an Adaptive Prior-Guided Network, which integrates a Siamese architecture with a novel prior-guided mechanism to enhance robustness and detection accuracy. First, we employ the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm for data augmentation, generating illumination-invariant images to mitigate degradation effects. Second, we design an Extended Receptive Field (ERF) module combined with a Multi-Scale Progressive Decoder (MPD) to capture multi-scale contextual information and refine feature representations. Furthermore, we propose an adaptive prior-guided mechanism that hierarchically fuses position and boundary priors by embedding spatial attention in high-level features for coarse localization and using deformable convolution to refine contours in low-level features. Extensive experimental results on two public MAS datasets demonstrate that our proposed method APGNet outperforms 15 state-of-art methods under widely used evaluation metrics.

Problem

• Detecting camouflaged objects in degraded underwater environments
• Addressing underwater image degradation with low contrast
• Overcoming limitations of terrestrial COD methods underwater

Innovation

• Adaptive prior-guided mechanism for hierarchical fusion
• MSRCR algorithm for illumination-invariant data augmentation
• Extended Receptive Field with Multi-Scale Progressive Decoder