Progressive Region-to-Boundary Exploration Network for Camouflaged Object Detection

作     者：Yue, Guanghui Wu, Shangjie Zhou, Tianwei Li, Gang Du, Jie Luo, Yu Jiang, Qiuping 

作者机构：Shenzhen Univ Sch Biomed Engn Guangdong Key Lab Biomed Measurements & Ultrasound Natl Reg Key Technol Engn Lab Med UltrasoundMed S Shenzhen 518060 Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen 518060 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Baicheng Normal Univ Sch Mech & Control Engn Baicheng 137000 Peoples R China Guangdong Univ Technol Sch Comp Sci Guangzhou 510006 Peoples R China Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON MULTIMEDIA》 (IEEE Trans Multimedia)

年 卷 期：2025年第27卷

页      面：236-248页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [62371305, 62103286] Guangdong Basic and Applied Basic Research Foundation [2024A1515030025, 2024A1515030278, 2022A1515010160] Shenzhen Medical Research Fund [A2403035] Natural Science Foundation of Shenzhen [JCYJ20230808105906013] Medicine Plus Program of Shenzhen University [2024YG007] Tencent "Rhinoceros Birds"- Scientific Research Foundation for Young Teachers of Shenzhen University Natural Science Foundation of Zhejiang [LR22F020002] Natural Science Foundation of Ningbo [2022J081] 

主　　题：Feature extraction Decoding Object detection Shape Biomedical imaging Accuracy Visualization Ultrasonic imaging Search problems Iterative methods Camouflaged object detection deep neural network region and boundary exploration region enhancement boundary refinement 

摘      要：Camouflaged object detection (COD) aims to segment targeted objects that have similar colors, textures, or shapes to their background environment. Due to the limited ability in distinguishing highly similar patterns, existing COD methods usually produce inaccurate predictions, especially around the boundary areas, when coping with complex scenes. This paper proposes a Progressive Region-to-Boundary Exploration Network (PRBE-Net) to accurately detect camouflaged objects. PRBE-Net follows an encoder-decoder framework and includes three key modules. Specifically, firstly, both high-level and low-level features of the encoder are integrated by a region and boundary exploration module to explore their complementary information for extracting the object s coarse region and fine boundary cues simultaneously. Secondly, taking the region cues as the guidance information, a Region Enhancement (RE) module is used to adaptively localize and enhance the region information at each layer of the encoder. Subsequently, considering that camouflaged objects usually have blurry boundaries, a Boundary Refinement (BR) decoder is used after the RE module to better detect the boundary areas with the assistance of boundary cues. Through top-down deep supervision, PRBE-Net can progressively refine the prediction. Extensive experiments on four datasets indicate that our PRBE-Net achieves superior results over 21 state-of-the-art COD methods. Additionally, it also shows good results on polyp segmentation, a COD-related task in the medical field.