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.
Abstract (translated)
在水下环境中检测伪装物体对于海洋生态研究和资源勘探至关重要。然而，现有方法面临着两个主要挑战：水中图像退化（包括低对比度和颜色失真）以及海洋生物的天然伪装。传统图像增强技术难以恢复受损图像中的关键特征，而为陆地场景开发的伪装物体检测(COD) 方法由于缺乏对水下光学特性的考虑，往往无法适应水下环境。为了应对这些问题，我们提出了一种自适应先验引导网络（APGNet），它集成了孪生架构与新颖的先验指导机制来提高鲁棒性和检测精度。 首先，我们采用多尺度Retinex彩色恢复（MSRCR）算法进行数据增强，生成照明不变图像以减轻退化效应。其次，我们设计了一个扩展感受野（ERF）模块结合了多尺度渐进解码器（MPD），用于捕捉多尺度上下文信息并细化特征表示。此外，我们还提出了一种自适应先验引导机制，通过在高级别特征中嵌入空间注意来进行位置和边界先验的层次融合，进行粗定位，并使用可变形卷积来精细低级别特征中的轮廓。 在两个公开的MAS数据集上的大量实验结果表明，在广泛使用的评估指标下，我们提出的方法APGNet优于15种最先进的方法。
URL
https://arxiv.org/abs/2510.12056
