{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:50:18Z","timestamp":1762509018090,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Xinjiang Province","award":["2020D01C026","2022D04079","U1911401","61433012"],"award-info":[{"award-number":["2020D01C026","2022D04079","U1911401","61433012"]}]},{"name":"open project of key laboratory, Xinjiang Uygur Autonomous Region","award":["2020D01C026","2022D04079","U1911401","61433012"],"award-info":[{"award-number":["2020D01C026","2022D04079","U1911401","61433012"]}]},{"name":"National Natural Science Foundation of China","award":["2020D01C026","2022D04079","U1911401","61433012"],"award-info":[{"award-number":["2020D01C026","2022D04079","U1911401","61433012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (S\u03b1, E\u03d5, F\u03b2w, and MAE).<\/jats:p>","DOI":"10.3390\/s23135789","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T02:09:17Z","timestamp":1687399757000},"page":"5789","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Edge-Guided Camouflaged Object Detection via Multi-Level Feature Integration"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2807-6199","authenticated-orcid":false,"given":"Kangwei","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianchi","family":"Qiu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3089-4140","authenticated-orcid":false,"given":"Yinfeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songlin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuhong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Sun, G., Cheng, M.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_2","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., and Huang, Q. (2019, January 15\u201320). Cascaded partial decoder for fast and accurate salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00403"},{"key":"ref_3","unstructured":"Zhao, J.X., Liu, J.J., Fan, D.P., Cao, Y., Yang, J., and Cheng, M.M. (November, January 27). EGNet: Edge guidance network for salient object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Zhang, J., Fan, D.P., Dai, Y., Anwar, S., Saleh, F.S., Zhang, T., and Barnes, N. (2020, January 13\u201319). UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00861"},{"key":"ref_6","unstructured":"Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., and Shao, L. (2020). Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2020, Proceedings of the 23rd International Conference, Lima, Peru, 4\u20138 October 2020, Springer. Proceedings, Part VI 23."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1109\/TIP.2021.3058783","article-title":"Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation","volume":"30","author":"Wu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fuentes, A., Yoon, S., Kim, S.C., and Park, D.S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17.","DOI":"10.3390\/s17092022"},{"key":"ref_9","first-page":"1","article-title":"A small-sized object detection oriented multi-scale feature fusion approach with application to defect detection","volume":"71","author":"Zeng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"77","DOI":"10.5772\/60526","article-title":"Investigation of vision-based underwater object detection with multiple datasets","volume":"12","author":"Rizzini","year":"2015","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_11","first-page":"1060","article-title":"An efficient content based image retrieval using color and texture of image sub blocks","volume":"3","author":"Kavitha","year":"2011","journal-title":"Int. J. Eng. Sci. Technol. (IJEST)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15884","DOI":"10.1109\/ACCESS.2019.2894420","article-title":"A high-efficiency fully convolutional networks for pixel-wise surface defect detection","volume":"7","author":"Qiu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Siricharoen, P., Aramvith, S., Chalidabhongse, T., and Siddhichai, S. (2010, January 21\u201323). Robust outdoor human segmentation based on color-based statistical approach and edge combination. Proceedings of the The 2010 International Conference on Green Circuits and Systems, Shanghai, China.","DOI":"10.1109\/ICGCS.2010.5543017"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"152","DOI":"10.5539\/mas.v5n4p152","article-title":"Study on the camouflaged target detection method based on 3D convexity","volume":"5","author":"Pan","year":"2011","journal-title":"Mod. Appl. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"43290","DOI":"10.1109\/ACCESS.2021.3064443","article-title":"Mirrornet: Bio-inspired camouflaged object segmentation","volume":"9","author":"Yan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, J., Zhang, X., Zhang, S., and Liu, J. (2021, January 2\u20139). Inferring camouflaged objects by texture-aware interactive guidance network. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i4.16475"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., and Fan, D.P. (2021, January 20\u201325). Mutual graph learning for camouflaged object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01280"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, F., Zhai, Q., Li, X., Huang, R., Luo, A., Cheng, H., and Fan, D.P. (2021, January 11\u201317). Uncertainty-guided transformer reasoning for camouflaged object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00411"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108414","DOI":"10.1016\/j.patcog.2021.108414","article-title":"Fast camouflaged object detection via edge-based reversible re-calibration network","volume":"123","author":"Ji","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mei, H., Ji, G.P., Wei, Z., Yang, X., Wei, X., and Fan, D.P. (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_22","doi-asserted-by":"crossref","unstructured":"Sun, Y., Chen, G., Zhou, T., Zhang, Y., and Liu, N. (2021). Context-aware cross-level fusion network for camouflaged object detection. arXiv.","DOI":"10.24963\/ijcai.2021\/142"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5364","DOI":"10.1109\/TIE.2021.3078379","article-title":"D 2 C-Net: A Dual-Branch, Dual-Guidance and Cross-Refine Network for Camouflaged Object Detection","volume":"69","author":"Wang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7036","DOI":"10.1109\/TIP.2022.3217695","article-title":"Feature Aggregation and Propagation Network for Camouflaged Object Detection","volume":"31","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, A., Zhang, J., Lv, Y., Liu, B., Zhang, T., and Dai, Y. (2021, January 20\u201325). Uncertainty-aware joint salient object and camouflaged object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00994"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., and Fan, D.P. (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_27","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_28","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Xiang, T.Z., Zhang, L., and Lu, H. (2022, January 18\u201322). 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_29","doi-asserted-by":"crossref","unstructured":"Luo, Z., Mishra, A., Achkar, A., Eichel, J., Li, S., and Jodoin, P.M. (2017, January 21\u201326). Non-local deep features for salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA.","DOI":"10.1109\/CVPR.2017.698"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, T., Qi, J., Lu, H., and Wang, G. (2018, January 18\u201323). Progressive Attention Guided Recurrent Network for Salient Object Detection. Proceedings of the CVPR, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00081"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, S., Tan, X., Wang, B., and Hu, X. (2018, January 8\u201314). Reverse attention for salient object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_15"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, R., Feng, M., Guan, W., Wang, D., Lu, H., and Ding, E. (2019, January 15\u201320). A mutual learning method for salient object detection with intertwined multi-supervision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00834"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, D., Lu, H., Wang, H., and Ruan, X. (2017, January 22\u201329). Amulet: Aggregating multi-level convolutional features for salient object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.31"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., and Torr, P.H. (2017, January 21\u201326). Deeply supervised salient object detection with short connections. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.563"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, L., Wang, S., Lu, H., Yang, G., Ruan, X., and Borji, A. (2018, January 18\u201322). Detect globally, refine locally: A novel approach to saliency detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00330"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Zhang, L., and Lu, H. (2020, January 13\u201319). Multi-scale interactive network for salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"ref_37","unstructured":"Ding, H., Jiang, X., Liu, A.Q., Thalmann, N.M., and Wang, G. (November, January 27). Boundary-aware feature propagation for scene segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_38","unstructured":"Zhu, H., Li, P., Xie, H., Yan, X., Liang, D., Chen, D., Wei, M., and Qin, J. (March, January 22). I can find you! Boundary-guided separated attention network for camouflaged object detection. Proceedings of the AAAI Conference on Artificial Intelligence, virtual."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_41","unstructured":"Fan, D.P., Zhai, Y., Borji, A., Yang, J., and Shao, L. (2020). Proceedings of the Computer Vision\u2013ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer. Proceedings, Part XII."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1109\/TIP.2022.3164550","article-title":"EDN: Salient object detection via extremely-downsampled network","volume":"31","author":"Wu","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","unstructured":"Yin, B., Zhang, X., Hou, Q., Sun, B.Y., Fan, D.P., and Van Gool, L. (2022). CamoFormer: Masked Separable Attention for Camouflaged Object Detection. arXiv."},{"key":"ref_44","unstructured":"Lee, M.S., Shin, W., and Han, S.W. (March, January 22). TRACER: Extreme Attention Guided Salient Object Tracing Network (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, virtual."},{"key":"ref_45","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_46","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_47","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_48","first-page":"7","article-title":"Animal camouflage analysis: Chameleon database","volume":"2","author":"Skurowski","year":"2018","journal-title":"Unpubl. Manuscr."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Cheng, M.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_50","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., and Borji, A. (2018). Enhanced-alignment measure for binary foreground map evaluation. arXiv.","DOI":"10.24963\/ijcai.2018\/97"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Margolin, R., Zelnik-Manor, L., and Tal, A. (2014, January 23\u201328). How to evaluate foreground maps?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA.","DOI":"10.1109\/CVPR.2014.39"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6981","DOI":"10.1109\/TCSVT.2022.3178173","article-title":"Camouflaged object detection via context-aware cross-level fusion","volume":"32","author":"Chen","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_53","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., de Lange, T., Johansen, D., and Johansen, H.D. (2020). Proceedings of the MultiMedia Modeling, Proceedings of the 26th International Conference, MMM 2020, Daejeon, Republic of Korea, 5\u20138 January 2020, Springer. Proceedings, Part II 26."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic road crack detection using random structured forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_56","unstructured":"Xie, E., Wang, W., Wang, W., Ding, M., Shen, C., and Luo, P. (2020). Proceedings of the Computer Vision\u2013ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer. Proceedings, Part XIII 16."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5789\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:57:59Z","timestamp":1760126279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5789"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,21]]},"references-count":56,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135789"],"URL":"https:\/\/doi.org\/10.3390\/s23135789","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,6,21]]}}}