{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:13:12Z","timestamp":1780384392604,"version":"3.54.1"},"reference-count":56,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018AAA0100500"],"award-info":[{"award-number":["2018AAA0100500"]}]},{"name":"Jiangsu Provincial Key Laboratory of Network and Information Security under Grants","award":["No.BM2003201"],"award-info":[{"award-number":["No.BM2003201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The demand for the sensor-based detection of camouflage objects widely exists in biological research, remote sensing, and military applications. However, the performance of traditional object detection algorithms is limited, as they are incapable of extracting informative parts from low signal-to-noise ratio features. To address this problem, we propose Camouflaged Object Detection with Cascade and Feedback Fusion (CODCEF), a deep learning framework based on an RGB optical sensor that leverages a cascaded structure with Feedback Partial Decoders (FPD) instead of a traditional encoder\u2013decoder structure. Through a selective fusion strategy and feedback loop, FPD reduces the loss of information and the interference of noises in the process of feature interweaving. Furthermore, we introduce Pixel Perception Fusion (PPF) loss, which aims to pay more attention to local pixels that might become the edges of an object. Experimental results on an edge device show that CODCEF achieved competitive results compared with 10 state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s21165455","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T05:34:46Z","timestamp":1628832886000},"page":"5455","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Cascade and Fusion: A Deep Learning Approach for Camouflaged Object Sensing"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3229-5301","authenticated-orcid":false,"given":"Kaihong","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunshu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brown University, Providence, RI 02860, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beilun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, C., Li, T.H., and Li, G. 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