{"id":"https://openalex.org/W4411463549","doi":"https://doi.org/10.71097/ijsat.v16.i2.6154","title":"Computer Vision-Based Anomaly Detection in Industrial Components","display_name":"Computer Vision-Based Anomaly Detection in Industrial Components","publication_year":2025,"publication_date":"2025-06-12","ids":{"openalex":"https://openalex.org/W4411463549","doi":"https://doi.org/10.71097/ijsat.v16.i2.6154"},"language":"en","primary_location":{"id":"doi:10.71097/ijsat.v16.i2.6154","doi":"https://doi.org/10.71097/ijsat.v16.i2.6154","is_oa":true,"landing_page_url":"https://doi.org/10.71097/ijsat.v16.i2.6154","pdf_url":"https://www.ijsat.org/papers/2025/2/6154.pdf","source":{"id":"https://openalex.org/S5406984700","display_name":"International Journal on Science and Technology","issn_l":"2229-7677","issn":["2229-7677"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Science and Technology","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.ijsat.org/papers/2025/2/6154.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118396035","display_name":"Advaith Dinesan Pudussery -","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4387152553","display_name":"HKBK College of Engineering","ror":"https://ror.org/05ypjr115","country_code":null,"type":"education","lineage":["https://openalex.org/I4387152553"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Advaith Dinesan Pudussery -","raw_affiliation_strings":["Computer Science & Engineering HKBK College of Engineering , Bangalore , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering HKBK College of Engineering , Bangalore , India","institution_ids":["https://openalex.org/I4387152553"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Alen Roy -","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4387152553","display_name":"HKBK College of Engineering","ror":"https://ror.org/05ypjr115","country_code":null,"type":"education","lineage":["https://openalex.org/I4387152553"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Alen Roy -","raw_affiliation_strings":["Computer Science & Engineering HKBK College of Engineering , Bangalore , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering HKBK College of Engineering , Bangalore , India","institution_ids":["https://openalex.org/I4387152553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118396036","display_name":"Ihsan Rafeeque Sainudheen -","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4387152553","display_name":"HKBK College of Engineering","ror":"https://ror.org/05ypjr115","country_code":null,"type":"education","lineage":["https://openalex.org/I4387152553"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ihsan Rafeeque Sainudheen -","raw_affiliation_strings":["Computer Science & Engineering HKBK College of Engineering , Bangalore , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering HKBK College of Engineering , Bangalore , India","institution_ids":["https://openalex.org/I4387152553"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100497335","display_name":"Jithin P Joji -","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4387152553","display_name":"HKBK College of Engineering","ror":"https://ror.org/05ypjr115","country_code":null,"type":"education","lineage":["https://openalex.org/I4387152553"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jithin P Joji -","raw_affiliation_strings":["Computer Science & Engineering HKBK College of Engineering , Bangalore , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering HKBK College of Engineering , Bangalore , India","institution_ids":["https://openalex.org/I4387152553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044607615","display_name":"Smitha Kurian","orcid":"https://orcid.org/0000-0002-1115-4385","observed_orcids":["https://orcid.org/0000-0002-1115-4385"]},"institutions":[{"id":"https://openalex.org/I4387152553","display_name":"HKBK College of Engineering","ror":"https://ror.org/05ypjr115","country_code":null,"type":"education","lineage":["https://openalex.org/I4387152553"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Smitha Kurian -","raw_affiliation_strings":["Computer Science & Engineering HKBK College of Engineering , Bangalore , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science & Engineering HKBK College of Engineering , Bangalore , India","institution_ids":["https://openalex.org/I4387152553"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4387152553"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11328189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9994999766349792,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/anomaly-detection","display_name":"anomaly detection","score":0.8489999771118164},{"id":"https://openalex.org/keywords/industrial-components","display_name":"industrial components","score":0.8939999938011169},{"id":"https://openalex.org/keywords/automated-visual-inspection","display_name":"automated visual inspection","score":0.7129999995231628},{"id":"https://openalex.org/keywords/defect-detection","display_name":"defect detection","score":0.8159999847412109},{"id":"https://openalex.org/keywords/deep-neural-network","display_name":"deep neural network","score":0.7820000052452087},{"id":"https://openalex.org/keywords/feature-clustering","display_name":"feature clustering","score":0.7670000195503235},{"id":"https://openalex.org/keywords/real-time-inference","display_name":"real-time inference","score":0.8259999752044678},{"id":"https://openalex.org/keywords/lightweight-architecture","display_name":"lightweight architecture","score":0.7940000295639038},{"id":"https://openalex.org/keywords/rgb-d-fusion","display_name":"RGB-D fusion","score":0.7099999785423279},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.8930000066757202},{"id":"https://openalex.org/keywords/distribution-shift","display_name":"distribution shift","score":0.9110000133514404},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.9179999828338623},{"id":"https://openalex.org/keywords/hybrid-learning","display_name":"hybrid learning","score":0.7950000166893005},{"id":"https://openalex.org/keywords/benchmark-dataset","display_name":"benchmark dataset","score":0.6019999980926514},{"id":"https://openalex.org/keywords/evaluation-indicators","display_name":"evaluation metrics","score":0.6140000224113464}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7649415135383606},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7629073858261108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6510348916053772},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5524706244468689},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5312613844871521},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.48103123903274536},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4676933288574219},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.45631110668182373},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4519701302051544},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4276908040046692},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.415351539850235},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.414612740278244},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4110499322414398},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3620235323905945},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.71097/ijsat.v16.i2.6154","doi":"https://doi.org/10.71097/ijsat.v16.i2.6154","is_oa":true,"landing_page_url":"https://doi.org/10.71097/ijsat.v16.i2.6154","pdf_url":"https://www.ijsat.org/papers/2025/2/6154.pdf","source":{"id":"https://openalex.org/S5406984700","display_name":"International Journal on Science and Technology","issn_l":"2229-7677","issn":["2229-7677"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Science and Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.71097/ijsat.v16.i2.6154","doi":"https://doi.org/10.71097/ijsat.v16.i2.6154","is_oa":true,"landing_page_url":"https://doi.org/10.71097/ijsat.v16.i2.6154","pdf_url":"https://www.ijsat.org/papers/2025/2/6154.pdf","source":{"id":"https://openalex.org/S5406984700","display_name":"International Journal on Science and Technology","issn_l":"2229-7677","issn":["2229-7677"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal on Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5378999710083008}],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6000000238418579}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411463549.pdf","grobid_xml":"https://content.openalex.org/works/W4411463549.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513","https://openalex.org/W120741642","https://openalex.org/W138569904","https://openalex.org/W2390914021","https://openalex.org/W2389417819"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,75],"in":[2,19,79,108,114],"industrial":[3,125,150],"components":[4],"is":[5],"essential":[6],"for":[7,91,148],"ensuring":[8],"product":[9],"quality":[10],"and":[11,22,46,56,71,77,84,102,120,134,144],"operational":[12],"efficiency":[13],"by":[14],"identifying":[15],"defects":[16],"early.":[17],"Advances":[18],"computer":[20],"vision":[21],"deep":[23,51],"learning":[24],"have":[25],"transformed":[26],"traditional":[27],"inspection,":[28],"enabling":[29],"automated":[30],"systems":[31,147],"to":[32],"detect":[33],"subtle":[34],"anomalies":[35],"with":[36],"high":[37],"accuracy.":[38],"This":[39,127],"survey":[40,128],"reviews":[41],"recent":[42],"approaches,":[43],"including":[44],"supervised":[45],"unsupervised":[47],"methods,":[48],"focusing":[49],"on":[50,63],"neural":[52],"networks,":[53],"memory-augmented":[54],"models,":[55],"feature":[57,69],"clustering":[58],"techniques":[59],"that":[60],"reduce":[61],"reliance":[62],"labeled":[64],"anomaly":[65,151],"data.":[66],"Advanced":[67],"preprocessing,":[68],"extraction,":[70],"patch-wise":[72],"analysis":[73],"enhance":[74],"sensitivity":[76],"localization":[78],"high-resolution":[80],"images.":[81],"Real-time":[82],"frameworks":[83],"lightweight":[85],"architectures":[86],"allow":[87],"millisecond-level":[88],"inference,":[89],"suitable":[90],"production":[92],"lines.":[93],"The":[94],"fusion":[95],"of":[96],"multiple":[97],"data":[98,116],"modalities,":[99],"like":[100],"RGB":[101],"depth":[103],"data,":[104],"further":[105],"improves":[106],"robustness":[107],"complex":[109],"environments.":[110],"However,":[111],"challenges":[112],"remain":[113],"handling":[115],"imbalance,":[117],"distribution":[118],"shifts,":[119],"generalizing":[121],"models":[122],"across":[123],"diverse":[124],"settings.":[126],"discusses":[129],"benchmark":[130],"datasets,":[131],"evaluation":[132],"metrics,":[133],"future":[135],"research":[136],"directions,":[137],"such":[138],"as":[139],"explainable":[140],"AI,":[141],"hybrid":[142],"learning,":[143],"scalable":[145],"adaptive":[146],"next-generation":[149],"detection.":[152]},"counts_by_year":[],"updated_date":"2026-09-23T05:00:35.095934","created_date":"2025-10-10T00:00:00"}
