{"id":"https://openalex.org/W7128530972","doi":"https://doi.org/10.48550/arxiv.2602.07062","title":"From Images to Decisions: Assistive Computer Vision for Non-Metallic Content Estimation in Scrap Metal","display_name":"From Images to Decisions: Assistive Computer Vision for Non-Metallic Content Estimation in Scrap Metal","publication_year":2026,"publication_date":"2026-02-05","ids":{"openalex":"https://openalex.org/W7128530972","doi":"https://doi.org/10.48550/arxiv.2602.07062"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.07062","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119620031","display_name":"Daniil Storonkin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Storonkin, Daniil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125527962","display_name":"Ilia Dziub","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dziub, Ilia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036978455","display_name":"Maksim Golyadkin","orcid":"https://orcid.org/0000-0002-0679-6981"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golyadkin, Maksim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5074238659","display_name":"Ilya Makarov","orcid":"https://orcid.org/0000-0002-3308-8825"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Makarov, Ilya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10914499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T12282","display_name":"Mineral Processing and Grinding","score":0.392300009727478,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.392300009727478,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.10050000250339508,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.05130000039935112,"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/scrap","display_name":"Scrap","score":0.9441999793052673},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7035999894142151},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.392300009727478},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.37459999322891235},{"id":"https://openalex.org/keywords/hazardous-waste","display_name":"Hazardous waste","score":0.36910000443458557},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.3407999873161316},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.3407000005245209},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.33869999647140503}],"concepts":[{"id":"https://openalex.org/C192097918","wikidata":"https://www.wikidata.org/wiki/Q917714","display_name":"Scrap","level":2,"score":0.9441999793052673},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7035999894142151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5335999727249146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5185999870300293},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4366999864578247},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C22507642","wikidata":"https://www.wikidata.org/wiki/Q1069369","display_name":"Hazardous waste","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.34360000491142273},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C58581272","wikidata":"https://www.wikidata.org/wiki/Q12741163","display_name":"Workspace","level":3,"score":0.3183000087738037},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2515000104904175},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.07062","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.07062","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.07062","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.07062","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.517702043056488,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Scrap":[0],"quality":[1],"directly":[2],"affects":[3],"energy":[4],"use,":[5],"emissions,":[6],"and":[7,29,34,55,73,81,90,95,136,146,164,169],"safety":[8],"in":[9,113],"steelmaking.":[10],"Today,":[11],"the":[12,70,111,118],"share":[13],"of":[14],"non-metallic":[15],"inclusions":[16],"(contamination)":[17],"is":[18,27],"judged":[19],"visually":[20],"by":[21,93,140],"inspectors":[22],"-":[23],"an":[24,39,96,150],"approach":[25],"that":[26,44],"subjective":[28,159],"hazardous":[30],"due":[31],"to":[32],"dust":[33],"moving":[35],"machinery.":[36],"We":[37],"present":[38,110],"assistive":[40],"computer":[41],"vision":[42],"pipeline":[43,157],"estimates":[45,132],"contamination":[46,63],"(per":[47],"percent)":[48],"from":[49],"images":[50],"captured":[51],"during":[52],"railcar":[53,71],"unloading":[54],"also":[56],"classifies":[57],"scrap":[58,106],"type.":[59],"The":[60,156],"method":[61],"formulates":[62],"assessment":[64],"as":[65],"a":[66,126],"regression":[67],"task":[68],"at":[69],"level":[72],"leverages":[74],"sequential":[75],"data":[76],"through":[77],"multi-instance":[78],"learning":[79,83],"(MIL)":[80],"multi-task":[82],"(MTL).":[84],"Best":[85],"results":[86,137],"include":[87],"MAE":[88,100],"0.27":[89],"R2":[91],"0.83":[92],"MIL;":[94],"MTL":[97],"setup":[98],"reaches":[99],"0.36":[101],"with":[102,133,142],"F1":[103],"0.79":[104],"for":[105,153],"class.":[107],"Also":[108],"we":[109],"system":[112],"near":[114],"real":[115],"time":[116],"within":[117],"acceptance":[119,168],"workflow:":[120],"magnet/railcar":[121],"detection":[122],"segments":[123],"temporal":[124],"layers,":[125],"versioned":[127],"inference":[128],"service":[129],"produces":[130],"railcar-level":[131],"confidence":[134],"scores,":[135],"are":[138],"reviewed":[139],"operators":[141],"structured":[143],"overrides;":[144],"corrections":[145],"uncertain":[147],"cases":[148],"feed":[149],"active-learning":[151],"loop":[152],"continual":[154],"improvement.":[155],"reduces":[158],"variability,":[160],"improves":[161],"human":[162],"safety,":[163],"enables":[165],"integration":[166],"into":[167],"melt-planning":[170],"workflows.":[171]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-11T00:00:00"}
