MSRNet：一种用于伪装目标检测的多尺度递归网络

MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

November 16, 2025

作者: Leena Alghamdi, Muhammad Usman, Hafeez Anwar, Abdul Bais, Saeed Anwar

cs.AI

摘要

伪装目标检测是一项新兴且具有挑战性的计算机视觉任务，其核心在于识别并分割那些因颜色、纹理及尺寸高度相似而与环境融为一体的目标。该任务在弱光条件、部分遮挡、小目标尺寸、复杂背景图案及多目标共存等复杂场景下尤为困难。尽管已有多种精密方法被提出，现有技术仍难以在复杂场景中精准检测伪装目标，特别是在处理小目标和多目标时表现不佳，表明该领域仍有提升空间。我们提出一种多尺度递归网络，通过金字塔视觉Transformer主干网络提取多尺度特征，并利用基于注意力的尺度融合单元进行选择性特征融合。为实现更精确的目标检测，解码器通过多粒度融合单元递归优化特征。我们还开发了新颖的递归反馈解码策略以增强全局上下文理解，帮助模型克服任务中的挑战。通过联合利用多尺度学习和递归特征优化，所提方法实现了性能提升，成功检测出小尺寸及多目标伪装物体。我们的模型在两个伪装目标检测基准数据集上取得了最先进的结果，并在其余两个数据集上位列第二。相关代码、模型权重及结果已开源：https://github.com/linaagh98/MSRNet。

English

Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due to high similarity in color, texture, and size. This task is further complicated by low-light conditions, partial occlusion, small object size, intricate background patterns, and multiple objects. While many sophisticated methods have been proposed for this task, current methods still struggle to precisely detect camouflaged objects in complex scenarios, especially with small and multiple objects, indicating room for improvement. We propose a Multi-Scale Recursive Network that extracts multi-scale features via a Pyramid Vision Transformer backbone and combines them via specialized Attention-Based Scale Integration Units, enabling selective feature merging. For more precise object detection, our decoder recursively refines features by incorporating Multi-Granularity Fusion Units. A novel recursive-feedback decoding strategy is developed to enhance global context understanding, helping the model overcome the challenges in this task. By jointly leveraging multi-scale learning and recursive feature optimization, our proposed method achieves performance gains, successfully detecting small and multiple camouflaged objects. Our model achieves state-of-the-art results on two benchmark datasets for camouflaged object detection and ranks second on the remaining two. Our codes, model weights, and results are available at https://github.com/linaagh98/MSRNet{https://github.com/linaagh98/MSRNet}.