{"id":"https://openalex.org/W2980925565","doi":"https://doi.org/10.1109/etfa.2019.8869084","title":"Vision-Based Crack Detection using Transfer Learning in Metal Forming Processes","display_name":"Vision-Based Crack Detection using Transfer Learning in Metal Forming Processes","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2980925565","doi":"https://doi.org/10.1109/etfa.2019.8869084","mag":"2980925565"},"language":"en","primary_location":{"id":"doi:10.1109/etfa.2019.8869084","doi":"https://doi.org/10.1109/etfa.2019.8869084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa.2019.8869084","pdf_url":null,"source":{"id":"https://openalex.org/S4363607916","display_name":"IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5004499145","display_name":"Dominik Mittel","orcid":"https://orcid.org/0009-0002-3374-7330","observed_orcids":["https://orcid.org/0009-0002-3374-7330"]},"institutions":[{"id":"https://openalex.org/I142270456","display_name":"Technische Hochschule Augsburg","ror":"https://ror.org/016604a03","country_code":"DE","type":"education","lineage":["https://openalex.org/I142270456"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dominik Mittel","raw_affiliation_strings":["Faculty of Electrical Engineering, University of Applied Sciences Augsburg, Augsburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, University of Applied Sciences Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I142270456"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087945190","display_name":"F. Kerber","orcid":"https://orcid.org/0000-0002-4553-5186","observed_orcids":["https://orcid.org/0000-0002-4553-5186"]},"institutions":[{"id":"https://openalex.org/I142270456","display_name":"Technische Hochschule Augsburg","ror":"https://ror.org/016604a03","country_code":"DE","type":"education","lineage":["https://openalex.org/I142270456"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Florian Kerber","raw_affiliation_strings":["Faculty of Electrical Engineering, University of Applied Sciences Augsburg, Augsburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Electrical Engineering, University of Applied Sciences Augsburg, Augsburg, Germany","institution_ids":["https://openalex.org/I142270456"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I142270456"],"apc_list":null,"apc_paid":null,"fwci":1.6024,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.8021978,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"544","last_page":"551"},"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":0.9990000128746033,"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":0.9990000128746033,"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":0.0005000000237487257,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.00039999998989515007,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/crack-detection","display_name":"crack detection","score":0.996999979019165},{"id":"https://openalex.org/keywords/metal-forming","display_name":"metal forming","score":0.9100000262260437},{"id":"https://openalex.org/keywords/automated-visual-inspection","display_name":"automated visual inspection","score":0.8270000219345093},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.8040000200271606},{"id":"https://openalex.org/keywords/alexnet","display_name":"AlexNet","score":0.753000020980835},{"id":"https://openalex.org/keywords/googlenet","display_name":"GoogLeNet","score":0.9990000128746033},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8700000047683716},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.7170000076293945},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.8489999771118164},{"id":"https://openalex.org/keywords/quality-control","display_name":"quality control","score":0.746999979019165},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.5}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9214860200881958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7766369581222534},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.7668132781982422},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7320573329925537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.698911726474762},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5691573023796082},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.528105616569519},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.5158137679100037},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4811862111091614},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4666713774204254},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4640433192253113},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.41715461015701294},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39389878511428833},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3258209228515625},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08872270584106445},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/etfa.2019.8869084","doi":"https://doi.org/10.1109/etfa.2019.8869084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/etfa.2019.8869084","pdf_url":null,"source":{"id":"https://openalex.org/S4363607916","display_name":"IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.651199996471405,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2079054397","https://openalex.org/W2084524982","https://openalex.org/W2097117768","https://openalex.org/W2099244020","https://openalex.org/W2130723490","https://openalex.org/W2133059825","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2253429366","https://openalex.org/W2511065100","https://openalex.org/W2598457882","https://openalex.org/W2614221804","https://openalex.org/W2633025415","https://openalex.org/W2749684264","https://openalex.org/W2767106145","https://openalex.org/W2786174052","https://openalex.org/W2896969435","https://openalex.org/W2963655672","https://openalex.org/W7098642242"],"related_works":["https://openalex.org/W4399971159","https://openalex.org/W3195886526","https://openalex.org/W3166329543","https://openalex.org/W3167439812","https://openalex.org/W3158495635","https://openalex.org/W4310720391","https://openalex.org/W4297347642","https://openalex.org/W2919816425","https://openalex.org/W3151747677","https://openalex.org/W4366977959"],"abstract_inverted_index":{"Crack":[0],"detection":[1,42],"is":[2,19,25,43,64],"an":[3,35,147,152],"important":[4],"quality":[5,17,49],"control":[6,50],"task":[7],"for":[8,40,117,138,156],"metal":[9,54],"working":[10],"processes.":[11],"In":[12,32],"small":[13],"and":[14,28,71,92,98,120,130,145,151,161,178],"medium-size":[15],"businesses":[16],"inspection":[18,38],"often":[20],"performed":[21],"manually.":[22],"This":[23],"approach":[24],"time":[26],"consuming":[27],"sensitive":[29],"to":[30,85,103,121,132],"errors.":[31],"this":[33],"work":[34],"automated":[36],"visual":[37],"system":[39],"defect":[41],"presented":[44],"which":[45],"classifies":[46],"cracks":[47],"in":[48],"images":[51,100],"of":[52,106,113,149,154,167],"a":[53,77,172],"forming":[55],"process":[56],"taken":[57],"under":[58],"varying":[59],"ambient":[60],"conditions.":[61],"The":[62],"classification":[63,136,176],"based":[65,75],"on":[66,76,175],"transfer":[67],"learning":[68,74],"with":[69,95],"AlexNet":[70,144],"GoogLeNet.":[72],"Supervised":[73],"stochastic":[78],"gradient":[79],"descent":[80],"(sgd)":[81],"solver":[82],"was":[83],"used":[84],"train":[86],"the":[87,107,134,139,168],"convolutional":[88],"neural":[89],"network.":[90],"Oversampling":[91],"data":[93,115],"augmentation":[94],"rotated,":[96],"scaled":[97],"shifted":[99],"were":[101,128],"applied":[102],"overcome":[104],"limitations":[105],"available":[108],"dataset":[109],"like":[110],"huge":[111],"imbalances":[112],"training":[114,126,169],"sizes":[116],"different":[118],"classes":[119],"prevent":[122],"overfitting,":[123],"respectively.":[124],"Different":[125],"parameters":[127,170],"compared":[129],"tested":[131],"obtain":[133],"best":[135],"results":[137],"specific":[140],"task.":[141],"GoogLeNet":[142],"outperformed":[143],"reached":[146],"accuracy":[148,177],"0.998":[150],"F1-score":[153],"0.836":[155],"crack":[157],"detection.":[158],"Data":[159],"pre-processing":[160],"labeling":[162],"as":[163,165],"well":[164],"tuning":[166],"had":[171],"significant":[173],"influence":[174],"require":[179],"human":[180],"decision":[181],"making.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-10-25T00:00:00"}