{"id":"https://openalex.org/W2945490540","doi":"https://doi.org/10.1145/3318299.3318341","title":"Research on Texture Defect Detection Based on Faster-RCNN and Feature Fusion","display_name":"Research on Texture Defect Detection Based on Faster-RCNN and Feature Fusion","publication_year":2019,"publication_date":"2019-02-22","ids":{"openalex":"https://openalex.org/W2945490540","doi":"https://doi.org/10.1145/3318299.3318341","mag":"2945490540"},"language":"en","primary_location":{"id":"doi:10.1145/3318299.3318341","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318299.3318341","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 11th International Conference on Machine Learning and Computing","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/A5022612571","display_name":"Zhongkang Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongkang Lin","raw_affiliation_strings":["School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei Province, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101754212","display_name":"Zhiqiang Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Guo","raw_affiliation_strings":["School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei Province, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101892128","display_name":"Jie Yang","orcid":"https://orcid.org/0000-0002-3901-7489"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yang","raw_affiliation_strings":["School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei Province, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei Province, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3261,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.84225562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"429","last_page":"433"},"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":1.0,"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":1.0,"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":0.9840999841690063,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9602000117301941,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7582371234893799},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7288427352905273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7141080498695374},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6023855209350586},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5616069436073303},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5374864339828491},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5260232090950012},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5197300910949707},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5024495124816895},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.48062437772750854},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4346492290496826},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4065607786178589},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3525767922401428},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14285221695899963}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7582371234893799},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7288427352905273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7141080498695374},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6023855209350586},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5616069436073303},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5374864339828491},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5260232090950012},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5197300910949707},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5024495124816895},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.48062437772750854},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4346492290496826},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4065607786178589},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3525767922401428},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14285221695899963},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3318299.3318341","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3318299.3318341","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 11th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1992117926","https://openalex.org/W2070407351","https://openalex.org/W2095430846","https://openalex.org/W2102605133","https://openalex.org/W2117539524","https://openalex.org/W2526468814","https://openalex.org/W2585123518","https://openalex.org/W2618530766"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W803346624"],"abstract_inverted_index":{"Product":[0],"texture":[1,110],"defect":[2,18,111,158],"detection":[3,19,22,159],"is":[4],"one":[5],"of":[6,66,136,160],"the":[7,16,21,26,32,51,57,63,67,73,78,90,94,104,115,121,125,146,156],"important":[8],"quality":[9],"inspection":[10],"procedures":[11],"in":[12,120],"industrial":[13],"production.":[14],"For":[15],"traditional":[17],"methods,":[20],"processes":[23],"are":[24,28,34,130],"cumbersome,":[25],"accuracies":[27],"not":[29,35],"high,":[30],"and":[31,45,61,97,151],"generalizations":[33],"strong.":[36],"This":[37,48],"paper":[38],"proposes":[39],"a":[40],"method":[41,49],"based":[42],"on":[43,103,133],"Faster-RCNN":[44],"feature":[46],"fusion.":[47],"uses":[50],"ResNet":[52],"network":[53,100],"model":[54,91,117,147],"to":[55,155],"extract":[56],"shared":[58],"convolution":[59],"feature,":[60],"combines":[62],"high-level":[64],"features":[65,75],"ROI":[68],"pooling":[69],"layer":[70,86],"output":[71],"with":[72],"low-level":[74],"obtained":[76],"by":[77,92],"direction":[79],"gradient":[80],"histogram":[81],"(HOG)":[82],"as":[83],"full":[84],"connection":[85],"input.":[87],"Then,":[88],"optimizing":[89],"adjusting":[93],"training":[95],"parameters":[96],"convolutional":[98],"neural":[99],"structure.":[101],"Experiments":[102],"German":[105],"Pattern":[106],"Recognition":[107],"Association":[108],"(GAPR)":[109],"dataset":[112],"show":[113,144],"that":[114,145],"proposed":[116],"has":[118,148],"improved":[119],"mAP":[122],"index.":[123],"Through":[124],"migration":[126],"learning":[127],"strategy,":[128],"experiments":[129],"carried":[131],"out":[132],"several":[134],"sets":[135],"actually":[137],"collected":[138],"data":[139],"sets.":[140],"The":[141],"experimental":[142],"results":[143],"good":[149],"adaptability":[150],"can":[152],"be":[153],"applied":[154],"surface":[157],"workpieces":[161],"under":[162],"different":[163],"conditions.":[164]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}