{"id":"https://openalex.org/W4402352761","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651051","title":"Edge-Guided Multilevel Feature Fusion Network for Lightweight Camouflaged Object Detection","display_name":"Edge-Guided Multilevel Feature Fusion Network for Lightweight Camouflaged Object Detection","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352761","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651051"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651051","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067983994","display_name":"Xingpeng Zhang","orcid":"https://orcid.org/0000-0001-6547-6374"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingpeng Zhang","raw_affiliation_strings":["Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033468930","display_name":"Meilin Gao","orcid":"https://orcid.org/0000-0001-6474-8518"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meilin Gao","raw_affiliation_strings":["Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113370338","display_name":"Guohai Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guohai Gao","raw_affiliation_strings":["Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013773565","display_name":"Xin Wang","orcid":"https://orcid.org/0000-0002-4688-2948"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Petroleum University,School of Computer Science and Software Engineering,Chengdu,China,610500","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067746723","display_name":"Qiuli Wang","orcid":"https://orcid.org/0000-0001-8639-5186"},"institutions":[{"id":"https://openalex.org/I151075929","display_name":"Army Medical University","ror":"https://ror.org/05w21nn13","country_code":"CN","type":"education","lineage":["https://openalex.org/I151075929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiuli Wang","raw_affiliation_strings":["The First Affiliated Hospital of Army Medical University,Department of Radiology,Chongging,China,400032"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The First Affiliated Hospital of Army Medical University,Department of Radiology,Chongging,China,400032","institution_ids":["https://openalex.org/I151075929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7179508209228516},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7123770117759705},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6523528099060059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5677910447120667},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5533065795898438},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5185956954956055},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46048033237457275},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4534561336040497},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4333956837654114}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179508209228516},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7123770117759705},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6523528099060059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5677910447120667},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5533065795898438},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5185956954956055},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46048033237457275},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4534561336040497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4333956837654114},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651051","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334111","display_name":"Innovation Fund","ror":null},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1968773971","https://openalex.org/W1982075130","https://openalex.org/W1994922096","https://openalex.org/W2151040689","https://openalex.org/W2339309815","https://openalex.org/W2526449353","https://openalex.org/W2603777577","https://openalex.org/W2788026998","https://openalex.org/W2943545929","https://openalex.org/W2963032190","https://openalex.org/W2963112696","https://openalex.org/W2963529609","https://openalex.org/W2963868681","https://openalex.org/W2982083293","https://openalex.org/W2988916019","https://openalex.org/W2989684653","https://openalex.org/W2998449272","https://openalex.org/W3034684132","https://openalex.org/W3035290198","https://openalex.org/W3035422681","https://openalex.org/W3092344722","https://openalex.org/W3094897602","https://openalex.org/W3164098653","https://openalex.org/W3168112135","https://openalex.org/W3173782971","https://openalex.org/W3176152216","https://openalex.org/W3195923711","https://openalex.org/W3203700770","https://openalex.org/W3210073375","https://openalex.org/W3212593512","https://openalex.org/W4220880800","https://openalex.org/W4283813802","https://openalex.org/W4285259878","https://openalex.org/W4304098552","https://openalex.org/W4312258849","https://openalex.org/W4386076055","https://openalex.org/W4402703046"],"related_works":["https://openalex.org/W2737719445","https://openalex.org/W2099421762","https://openalex.org/W2530546662","https://openalex.org/W4239098401","https://openalex.org/W3147584709","https://openalex.org/W2967030268","https://openalex.org/W2977677679","https://openalex.org/W2185253430","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Camouflaged":[0],"object":[1,20,45,99],"detection":[2,46],"(COD)":[3],"aims":[4],"to":[5,62,75,82,94,108],"accurately":[6],"recognize":[7],"targets":[8],"in":[9],"intricate":[10],"environments":[11],"that":[12],"blend":[13],"into":[14,110,123],"the":[15,89,111,116,120,124],"background.":[16],"Although":[17],"numerous":[18],"camouflage":[19],"identification":[21],"techniques":[22],"have":[23],"demonstrated":[24],"effectiveness,":[25],"they":[26],"often":[27],"possess":[28],"a":[29,37,54,69,96,103],"substantial":[30],"number":[31],"of":[32],"parameters.":[33],"Therefore,":[34],"we":[35,52,67,101],"propose":[36],"new":[38],"lightweight":[39,55],"edge-guided":[40,126],"multilevel":[41],"feature":[42,60,71,77,85],"fusion":[43],"camouflaged":[44],"network,":[47],"codenamed":[48],"as":[49],"LEMFNet.":[50],"Initially,":[51],"adopt":[53],"CNN":[56],"network":[57],"model":[58,64,91],"for":[59],"extraction":[61],"reduce":[63],"complexity.":[65],"Subsequently,":[66],"introduced":[68],"neighborhood":[70],"association":[72],"module":[73,106,128],"(NFAM)":[74],"integrate":[76,119],"information":[78],"from":[79],"different":[80],"stages":[81],"obtain":[83,95],"complementary":[84],"representations":[86],"and":[87,118,150],"enhance":[88],"overall":[90],"performance.":[92],"Furthermore,":[93],"more":[97],"complete":[98],"structure,":[100],"introduce":[102],"boundary":[104],"aggregation":[105,127],"(BAM)":[107],"delve":[109],"edge":[112,121],"semantics":[113],"associated":[114],"with":[115,138],"target":[117],"features":[122],"proposed":[125],"(EGAM).":[129],"Experiments":[130],"on":[131],"three":[132],"challenging":[133],"benchmarks":[134],"demonstrate":[135],"our":[136],"approach,":[137],"fewer":[139],"parameters,":[140],"achieves":[141],"comparable":[142],"or":[143],"superior":[144],"performance,":[145],"effectively":[146],"balancing":[147],"resource":[148],"utilization":[149],"accuracy.":[151]},"counts_by_year":[{"year":2026,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
