{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T10:58:40Z","timestamp":1763117920205,"version":"3.45.0"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Camouflaged Object Detection (COD) is a challenging computer vision task aimed at accurately identifying and segmenting objects seamlessly blended into their backgrounds. This task has broad applications across medical image segmentation, defect detection, agricultural image detection, security monitoring, and scientific research. Traditional COD methods often struggle with precise segmentation due to the high similarity between camouflaged objects and their surroundings. In this study, we introduce a Boundary-Guided Differential Attention Network (BDA-Net) to address these challenges. BDA-Net first extracts boundary features by fusing multi-scale image features and applying channel attention. Subsequently, it employs a differential attention mechanism, guided by these boundary features, to highlight camouflaged objects and suppress background information. The weighted features are then progressively fused to generate accurate camouflage object masks. Experimental results on the COD10K, NC4K, and CAMO datasets demonstrate that BDA-Net outperforms most state-of-the-art COD methods, achieving higher accuracy. Here we show that our approach improves detection accuracy by up to 3.6% on key metrics, offering a robust solution for precise camouflaged object segmentation.<\/jats:p>","DOI":"10.3390\/jimaging11110412","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T10:07:03Z","timestamp":1763114823000},"page":"412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Boundary-Guided Differential Attention: Enhancing Camouflaged Object Detection Accuracy"],"prefix":"10.3390","volume":"11","author":[{"given":"Hongliang","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China"}]},{"given":"Bolin","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4840-5635","authenticated-orcid":false,"given":"Sanxin","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xiao, F., Hu, S., Shen, Y., Fang, C., Huang, J., He, C., Tang, L., Yang, Z., and Li, X. (2024). A survey of camouflaged object detection and beyond. arXiv.","DOI":"10.26599\/AIR.2024.9150044"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fan, D., Ji, G., Sun, G., Cheng, M., Shen, J., and Shao, L. (2020, January 13\u201319). Camouflaged object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1109\/TCSVT.2021.3126591","article-title":"Deep texture-aware features for camouflaged object detection","volume":"33","author":"Ren","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mei, H., Ji, G., Wei, Z., Yang, X., Wei, X., and Fan, D. (2021, January 20\u201325). Camouflaged object segmentation with distraction mining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00866"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Xiang, T., Zhang, L., and Lu, H. (2022, January 18\u201324). Zoom in and out: A mixed-scale triplet network for camouflaged object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00220"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Chen, C., and Xiang, T. (2022). Boundary-guided camouflaged object detection. arXiv.","DOI":"10.24963\/ijcai.2022\/186"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15075","DOI":"10.1007\/s00521-023-08502-3","article-title":"Boundary-guided context-aware network for camouflaged object detection","volume":"35","author":"Xiao","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, F., Zhai, Q., Li, X., Huang, R., Luo, A., Cheng, H., and Fan, D. (2021, January 10\u201317). Uncertainty-guided transformer reasoning for camouflaged object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00411"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, J., and Shi, Y. (2023, January 23\u201325). EPANet: Edge-assisted Position Aware Attention Network for Camouflaged Object Detection. Proceedings of the 2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan.","DOI":"10.1109\/ICIIBMS60103.2023.10347619"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, Z., Jiang, P., Lin, L., and Deng, X. (2024, January 14\u201319). Edge attention learning for efficient camouflaged object detection. Proceedings of the ICASSP 2024\u20142024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Republic of Korea.","DOI":"10.1109\/ICASSP48485.2024.10448139"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhu, H., Li, P., Xie, H., Yan, X., Liang, D., Chen, D., Wei, M., and Qin, J. (2022, January 24\u201328). I can find you! boundary-guided separated attention network for camouflaged object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Pomona, CA, USA.","DOI":"10.1609\/aaai.v36i3.20273"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bayraktar, I., and Bakirci, M. (2025, January 26\u201328). Attention-Augmented YOLO11 for High-Precision Aircraft Detection in Synthetic Aperture Radar Imagery. Proceedings of the 2025 27th International Conference on Digital Signal Processing and Its Applications (DSPA), Moscow, Russia.","DOI":"10.1109\/DSPA64310.2025.10977903"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fan, B., Cong, K., and Zou, W. (2023, January 27\u201329). Dual Attention and Edge Refinement Network for Camouflaged Object Detection. Proceedings of the 2023 8th International Conference on Image, Vision and Computing (ICIVC), Dalian, China.","DOI":"10.1109\/ICIVC58118.2023.10270622"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"162403","DOI":"10.1007\/s11432-022-3592-8","article-title":"Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection","volume":"66","author":"Xiang","year":"2023","journal-title":"Sci. China Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Du, S., Yao, C., Kong, Y., and Yang, Y. (2023, January 27\u201329). BANet: Camouflaged Object Detection Based on Boundary Guidance and Multiple Attention Mechanisms. Proceedings of the 2023 9th Annual International Conference on Network and Information Systems for Computers (ICNISC), Wuhan, China.","DOI":"10.1109\/ICNISC60562.2023.00027"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/LSP.2024.3356416","article-title":"FINet: Frequency injection network for lightweight camouflaged object detection","volume":"31","author":"Liang","year":"2024","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104924","DOI":"10.1016\/j.imavis.2024.104924","article-title":"Depth awakens: A depth-perceptual attention fusion network for RGB-D camouflaged object detection","volume":"143","author":"Liu","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112051","DOI":"10.1016\/j.knosys.2024.112051","article-title":"SDRNet: Camouflaged object detection with independent reconstruction of structure and detail","volume":"299","author":"Guan","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108328","DOI":"10.1016\/j.engappai.2024.108328","article-title":"CCNet: Collaborative Camouflaged Object Detection via decoder-induced information interaction and supervision refinement network","volume":"133","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105358","DOI":"10.1016\/j.imavis.2024.105358","article-title":"EPFDNet: Camouflaged object detection with edge perception in frequency domain","volume":"154","author":"Fang","year":"2025","journal-title":"Image Vis. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_22","unstructured":"Wei, J., Wang, S., and Huang, Q. (2020, January 7\u201312). F3Net: Fusion, feedback and focus for salient object detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.cviu.2019.04.006","article-title":"Anabranch network for camouflaged object segmentation","volume":"184","author":"Le","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., and Fan, D. (2021, January 20\u201325). Simultaneously localize, segment and rank the camouflaged objects. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01142"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fan, D., Cheng, M., Liu, Y., Li, T., and Borji, A. (2017, January 22\u201329). Structure-measure: A new way to evaluate foreground maps. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.487"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., and Hornung, A. (2012, January 16\u201321). Saliency filters: Contrast based filtering for salient region detection. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20\u201325). Frequency-tuned salient region detection. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206596"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fan, D., Gong, C., Cao, Y., Ren, B., Cheng, M., and Borji, A. (2018). Enhanced-alignment measure for binary foreground map evaluation. arXiv.","DOI":"10.24963\/ijcai.2018\/97"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6024","DOI":"10.1109\/TPAMI.2021.3085766","article-title":"Concealed object detection","volume":"44","author":"Fan","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jia, Q., Yao, S., Liu, Y., Fan, X., Liu, R., and Luo, Z. (2022, January 18\u201324). Segment, magnify and reiterate: Detecting camouflaged objects the hard way. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00467"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4593","DOI":"10.1007\/s00371-022-02611-1","article-title":"Tprnet: Camouflaged object detection via transformer-induced progressive refinement network","volume":"39","author":"Zhang","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, Z., Tan, Y., and Wu, W. (2022, January 21\u201325). Boosting camouflaged object detection with dual-task interactive transformer. Proceedings of the 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada.","DOI":"10.1109\/ICPR56361.2022.9956724"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.patrec.2023.09.007","article-title":"Polarization-based camouflaged object detection","volume":"174","author":"Wang","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4934","DOI":"10.1109\/TCSVT.2023.3245883","article-title":"MSCAF-Net: A general framework for camouflaged object detection via learning multi-scale context-aware features","volume":"33","author":"Liu","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5444","DOI":"10.1109\/TCSVT.2023.3255304","article-title":"Go closer to see better: Camouflaged object detection via object area amplification and figure-ground conversion","volume":"33","author":"Xing","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, C., Li, K., Zhang, Y., Tang, L., Zhang, Y., Guo, Z., and Li, X. (2023, January 17\u201324). Camouflaged object detection with feature decomposition and edge reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02111"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, Z., Dai, H., Xiang, T., Wang, S., Chen, H., Qin, J., and Xiong, H. (2023, January 17\u201324). Feature shrinkage pyramid for camouflaged object detection with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00538"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"21108","DOI":"10.1109\/JSEN.2024.3401722","article-title":"Weighted dense semantic aggregation and explicit boundary modeling for camouflaged object detection","volume":"24","author":"Liang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/LSP.2023.3348390","article-title":"Efficient camouflaged object detection via progressive refinement network","volume":"31","author":"Zhang","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"110895","DOI":"10.1016\/j.patcog.2024.110895","article-title":"Camouflaged object detection via dual-branch fusion and dual self-similarity constraints","volume":"157","author":"Yang","year":"2025","journal-title":"Pattern Recognit."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/412\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T10:56:25Z","timestamp":1763117785000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/412"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["jimaging11110412"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11110412","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]}}}