{"id":"https://openalex.org/W2949538563","doi":"https://doi.org/10.1145/3325917.3325930","title":"Specular reflection Surface Defects Detection by using Deep Learning","display_name":"Specular reflection Surface Defects Detection by using Deep Learning","publication_year":2019,"publication_date":"2019-04-06","ids":{"openalex":"https://openalex.org/W2949538563","doi":"https://doi.org/10.1145/3325917.3325930","mag":"2949538563"},"language":"en","primary_location":{"id":"doi:10.1145/3325917.3325930","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3325917.3325930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Information System and Data Mining","raw_type":"proceedings-article"},"type":"article","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/A5102012571","display_name":"Zhong Zhang","orcid":"https://orcid.org/0000-0002-4911-6173"},"institutions":[{"id":"https://openalex.org/I136259955","display_name":"Toyohashi University of Technology","ror":"https://ror.org/04ezg6d83","country_code":"JP","type":"education","lineage":["https://openalex.org/I136259955"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhong Zhang","raw_affiliation_strings":["Dep. of Mechanical Engineering Toyohashi University of Technology, Toyohashi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dep. of Mechanical Engineering Toyohashi University of Technology, Toyohashi, Japan","institution_ids":["https://openalex.org/I136259955"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101526210","display_name":"Borui Zhang","orcid":"https://orcid.org/0000-0001-6010-9305"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Borui Zhang","raw_affiliation_strings":["Dep. of Mechanical Engineering, Northeastern University, Shenyang, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dep. of Mechanical Engineering, Northeastern University, Shenyang, P. R. China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072270755","display_name":"Takuma Akiduki","orcid":"https://orcid.org/0000-0002-2064-0633"},"institutions":[{"id":"https://openalex.org/I136259955","display_name":"Toyohashi University of Technology","ror":"https://ror.org/04ezg6d83","country_code":"JP","type":"education","lineage":["https://openalex.org/I136259955"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuma Akiduki","raw_affiliation_strings":["Dep. of Mechanical Engineering Toyohashi University of Technology, Toyohashi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dep. of Mechanical Engineering Toyohashi University of Technology, Toyohashi, Japan","institution_ids":["https://openalex.org/I136259955"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.442,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70470409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"6","last_page":"10"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T13049","display_name":"Surface Roughness and Optical Measurements","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9939000010490417,"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/specular-reflection","display_name":"Specular reflection","score":0.9305214285850525},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8474287390708923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7823813557624817},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7387546896934509},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7132897973060608},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6446756720542908},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6388990879058838},{"id":"https://openalex.org/keywords/reflection","display_name":"Reflection (computer programming)","score":0.6287996768951416},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49088674783706665},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4388166666030884},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4328135848045349},{"id":"https://openalex.org/keywords/specular-highlight","display_name":"Specular highlight","score":0.42436981201171875},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.423622190952301},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.22210457921028137},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06586742401123047},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0568595826625824}],"concepts":[{"id":"https://openalex.org/C118381688","wikidata":"https://www.wikidata.org/wiki/Q1079524","display_name":"Specular reflection","level":2,"score":0.9305214285850525},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8474287390708923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7823813557624817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7387546896934509},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7132897973060608},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6446756720542908},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6388990879058838},{"id":"https://openalex.org/C65682993","wikidata":"https://www.wikidata.org/wiki/Q1056451","display_name":"Reflection (computer programming)","level":2,"score":0.6287996768951416},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49088674783706665},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4388166666030884},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4328135848045349},{"id":"https://openalex.org/C50045419","wikidata":"https://www.wikidata.org/wiki/Q7575328","display_name":"Specular highlight","level":3,"score":0.42436981201171875},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.423622190952301},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.22210457921028137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06586742401123047},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0568595826625824},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3325917.3325930","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3325917.3325930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Information System and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W206745480","https://openalex.org/W2063509315","https://openalex.org/W2080964655","https://openalex.org/W2112796928","https://openalex.org/W2163605009","https://openalex.org/W2177914549","https://openalex.org/W2557283755","https://openalex.org/W2900680905","https://openalex.org/W2919115771","https://openalex.org/W3192292085"],"related_works":["https://openalex.org/W2081408094","https://openalex.org/W2175900582","https://openalex.org/W2135509629","https://openalex.org/W1611976505","https://openalex.org/W3141300019","https://openalex.org/W2101266052","https://openalex.org/W913384303","https://openalex.org/W2738091825","https://openalex.org/W4390012772","https://openalex.org/W3035953197"],"abstract_inverted_index":{"As":[0],"you":[1],"know":[2],"that":[3,60],"defects":[4],"inspection":[5],"of":[6,55,64,80],"specular":[7,14],"surface":[8,31,105],"is":[9,16,22,45,61],"very":[10,17],"difficult":[11],"because":[12],"its":[13],"reflection":[15,21],"strong":[18],"and":[19,40,67,108],"defects'":[20],"weaker.":[23],"And":[24],"the":[25,41,48,52,77],"existing":[26],"computer":[27,81],"vision-based":[28],"industrial":[29],"parts":[30],"defect":[32,106],"detection":[33],"methods":[34],"are":[35],"limited":[36],"by":[37],"environmental":[38],"factors,":[39],"image":[42,72],"preprocessing":[43],"process":[44],"complex.":[46],"On":[47],"other":[49],"hand,":[50],"with":[51],"rapid":[53,78],"development":[54,79],"Convolutional":[56],"Neural":[57],"Networks":[58],"(CNN)":[59],"one":[62],"type":[63],"deep":[65,86],"learning":[66],"has":[68,74],"excellent":[69],"performance":[70],"for":[71,104],"processing,":[73],"led":[75],"to":[76],"vision":[82],"research":[83],"based":[84],"on":[85],"learning.":[87],"In":[88],"this":[89],"paper,":[90],"we":[91],"proposed":[92],"an":[93],"ensemble":[94],"CNN":[95],"in":[96],"which":[97],"integrated":[98],"two":[99],"convolutional":[100],"neural":[101],"network":[102],"models":[103],"detection,":[107],"obtained":[109],"better":[110],"results.":[111]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
