{"id":"https://openalex.org/W7133293853","doi":"https://doi.org/10.48550/arxiv.2603.00368","title":"Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification","display_name":"Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7133293853","doi":"https://doi.org/10.48550/arxiv.2603.00368"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00368","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00368","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.00368","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043497241","display_name":"Hutama Arif Bramantyo","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bramantyo, Hutama Arif","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118176158","display_name":"Mukarram Ali Faridi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Faridi, Mukarram Ali","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127916543","display_name":"Rui Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Rui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087010360","display_name":"Clarissa M. Harris","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harris, Clarissa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127893711","display_name":"Yin Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5043497241"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.2117999941110611,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.2117999941110611,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12486","display_name":"Food Supply Chain Traceability","score":0.1785999983549118,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.1242000013589859,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7106999754905701},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6484000086784363},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5523999929428101},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5509999990463257},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44350001215934753},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.424699991941452},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.4068000018596649},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.3955000042915344},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.39329999685287476}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7727000117301941},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7106999754905701},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6484000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5999000072479248},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5523999929428101},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5509999990463257},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44350001215934753},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.424699991941452},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3955000042915344},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3089999854564667},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.3077000081539154},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28209999203681946},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00368","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00368","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.00368","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00368","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"we":[3,100,174],"present":[4],"a":[5,57,86,183],"meat":[6,20,28,63],"freshness":[7],"classification":[8,96],"framework":[9],"from":[10],"Red-Green-Blue":[11],"(RGB)":[12],"images":[13],"that":[14,37],"supports":[15],"both":[16],"packaged":[17],"and":[18,30,65,85,114,125,143,147,160,166],"unpackaged":[19],"datasets.":[21],"The":[22,44,74],"system":[23],"classifies":[24],"four":[25],"in-distribution":[26],"(ID)":[27],"classes":[29],"uses":[31],"an":[32,78],"out-of-distribution":[33],"(OOD)-aware":[34],"abstention":[35,171],"mechanism":[36],"flags":[38],"low-confidence":[39],"samples":[40],"as":[41,56],"No":[42],"Result.":[43],"pipeline":[45],"combines":[46],"U-Net-based":[47],"segmentation":[48,75],"with":[49],"deep":[50],"feature":[51],"classifiers.":[52],"Segmentation":[53],"is":[54,152],"used":[55],"preprocessing":[58],"step":[59],"to":[60],"isolate":[61],"the":[62,95,129,136,170],"region":[64],"reduce":[66],"background,":[67],"producing":[68,91],"more":[69],"consistent":[70],"inputs":[71,93],"for":[72,94,122,189],"classification.":[73],"module":[76],"achieved":[77],"Intersection":[79],"over":[80,169],"Union":[81],"(IoU)":[82],"of":[83,89],"75%":[84],"Dice":[87],"coefficient":[88],"82%,":[90],"standardized":[92],"stage.":[97],"For":[98],"classification,":[99],"benchmark":[101],"five":[102],"backbones:":[103],"Residual":[104],"Network-50":[105],"(ResNet-50),":[106],"Vision":[107],"Transformer-Base/16":[108],"(ViT-B/16),":[109],"Swin":[110],"Transformer-Tiny":[111],"(Swin-T),":[112],"EfficientNet-B0,":[113],"MobileNetV3-Small.":[115],"We":[116,155],"use":[117],"nested":[118],"5x3":[119],"cross-validation":[120],"(CV)":[121],"model":[123],"selection":[124],"hyperparameter":[126],"tuning.":[127],"On":[128],"held-out":[130],"ID":[131],"test":[132],"set,":[133],"EfficientNet-B0":[134],"achieves":[135],"highest":[137],"accuracy":[138],"(98.10%),":[139],"followed":[140],"by":[141],"ResNet-50":[142],"MobileNetV3-Small":[144],"(both":[145],"97.63%)":[146],"Swin-T":[148],"(97.51%),":[149],"while":[150],"ViT-B/16":[151],"lower":[153],"(94.42%).":[154],"additionally":[156],"evaluate":[157],"OOD":[158,164],"scoring":[159],"thresholding":[161],"using":[162,178],"standard":[163],"metrics":[165],"sensitivity":[167],"analysis":[168],"threshold.":[172],"Finally,":[173],"report":[175],"on-device":[176],"latency":[177],"TensorFlow":[179],"Lite":[180],"(TFLite)":[181],"on":[182],"smartphone,":[184],"highlighting":[185],"practical":[186],"accuracy-latency":[187],"trade-offs":[188],"future":[190],"deployment.":[191]},"counts_by_year":[],"updated_date":"2026-03-04T07:09:34.246503","created_date":"2026-03-04T00:00:00"}
