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

偽裝目標檢測是一項新興且具挑戰性的計算機視覺任務，其核心在於識別並分割那些因顏色、紋理和尺寸高度相似而與環境融為一體的物體。該任務在低光照條件、部分遮擋、微小目標尺寸、複雜背景紋理以及多目標共存等場景下尤為困難。儘管已有諸多先進方法被提出，現有技術在複雜場景（特別是涉及微小及多目標時）仍難以實現精確檢測，表明該領域存在改進空間。為此，我們提出一種多尺度遞歸網絡：通過金字塔視覺Transformer骨幹網絡提取多尺度特徵，並利用專注力驅動的尺度融合單元進行選擇性特徵融合。為提升檢測精度，解碼器採用多粒度融合單元進行遞歸式特徵優化。我們還創新性地設計了遞歸反饋解碼策略，以增強全局上下文理解能力，幫助模型克服任務難點。通過聯合利用多尺度學習與遞歸特徵優化，本方法實現了性能突破，成功檢測微小及多目標偽裝物體。在兩個偽裝目標檢測基準數據集上達到最優性能，其餘兩個數據集位列第二。相關代碼、模型權重與結果已開源於：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}.