{"id":"https://openalex.org/W2965113623","doi":"https://doi.org/10.3390/sym11081001","title":"Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation","display_name":"Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation","publication_year":2019,"publication_date":"2019-08-03","ids":{"openalex":"https://openalex.org/W2965113623","doi":"https://doi.org/10.3390/sym11081001","mag":"2965113623"},"language":"en","primary_location":{"id":"doi:10.3390/sym11081001","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11081001","pdf_url":"https://www.mdpi.com/2073-8994/11/8/1001/pdf?version=1564820490","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/11/8/1001/pdf?version=1564820490","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009326228","display_name":"Guixiong Liu","orcid":"https://orcid.org/0000-0003-3893-0409"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guixiong Liu","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027709444","display_name":"Binyuan He","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Binyuan He","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049484085","display_name":"Siyuang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuang Liu","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101947032","display_name":"Jian Huang","orcid":"https://orcid.org/0000-0002-9616-7918"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Huang","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009326228"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":0.6611,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76328833,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"11","issue":"8","first_page":"1001","last_page":"1001"},"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":0.995199978351593,"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":0.995199978351593,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9581000208854675,"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"}},{"id":"https://openalex.org/T14155","display_name":"Advanced Technology in Applications","score":0.919700026512146,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/chassis","display_name":"Chassis","score":0.9786090850830078},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7078772187232971},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.6705020666122437},{"id":"https://openalex.org/keywords/image-stitching","display_name":"Image stitching","score":0.6505578756332397},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6403400301933289},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6326678991317749},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5925413370132446},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.49898219108581543},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45849788188934326},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4175954759120941},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41630619764328003},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41132214665412903},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33528071641921997},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20654717087745667}],"concepts":[{"id":"https://openalex.org/C512993513","wikidata":"https://www.wikidata.org/wiki/Q1068107","display_name":"Chassis","level":2,"score":0.9786090850830078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7078772187232971},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.6705020666122437},{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.6505578756332397},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6403400301933289},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6326678991317749},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5925413370132446},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.49898219108581543},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45849788188934326},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4175954759120941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41630619764328003},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41132214665412903},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33528071641921997},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20654717087745667},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym11081001","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11081001","pdf_url":"https://www.mdpi.com/2073-8994/11/8/1001/pdf?version=1564820490","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f55460d8fe58419b842b1aac5ae82858","is_oa":true,"landing_page_url":"https://doaj.org/article/f55460d8fe58419b842b1aac5ae82858","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 11, Iss 8, p 1001 (2019)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/11/8/1001/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/sym11081001","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym11081001","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym11081001","pdf_url":"https://www.mdpi.com/2073-8994/11/8/1001/pdf?version=1564820490","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2965113623.pdf","grobid_xml":"https://content.openalex.org/works/W2965113623.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W845365781","https://openalex.org/W1491742321","https://openalex.org/W1536680647","https://openalex.org/W1903029394","https://openalex.org/W2015475217","https://openalex.org/W2029021861","https://openalex.org/W2069060684","https://openalex.org/W2102605133","https://openalex.org/W2125629257","https://openalex.org/W2163605009","https://openalex.org/W2176950688","https://openalex.org/W2278186031","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2460583509","https://openalex.org/W2469673809","https://openalex.org/W2573897246","https://openalex.org/W2613718673","https://openalex.org/W2724924816","https://openalex.org/W2737449757","https://openalex.org/W2755930428","https://openalex.org/W2794550100","https://openalex.org/W2796813206","https://openalex.org/W2803244048","https://openalex.org/W2804451008","https://openalex.org/W2806646158","https://openalex.org/W2890127379","https://openalex.org/W2891391275","https://openalex.org/W2895362126","https://openalex.org/W2897063534","https://openalex.org/W2903875745","https://openalex.org/W2905511418","https://openalex.org/W2906970340","https://openalex.org/W2908633847","https://openalex.org/W2910558104","https://openalex.org/W2914321566","https://openalex.org/W2920376723","https://openalex.org/W2953384591","https://openalex.org/W2963150697","https://openalex.org/W2963556179","https://openalex.org/W2963659353","https://openalex.org/W2963857746","https://openalex.org/W2963881378","https://openalex.org/W3104282073","https://openalex.org/W3105043467","https://openalex.org/W6640054144","https://openalex.org/W6687483927","https://openalex.org/W6713134421","https://openalex.org/W6749698238"],"related_works":["https://openalex.org/W68020613","https://openalex.org/W1973382465","https://openalex.org/W4255291540","https://openalex.org/W3134240150","https://openalex.org/W2091466534","https://openalex.org/W4387591196","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Chassis":[0],"assembly":[1,74,80,96,130,157,165,176,194,220],"quality":[2,10,28,40,53,97,158],"is":[3,30,90,99,131,134,146,160,186],"a":[4,76,125,140],"necessary":[5],"step":[6],"to":[7,63,162,180],"improve":[8],"product":[9,27],"and":[11,36,43,57,69,82,121,152,156,167,183],"yield.":[12],"In":[13,92],"recent":[14],"years,":[15],"with":[16,136],"the":[17,44,48,64,93,103,115,149,153,171,184,201,204,211],"continuous":[18],"expansion":[19],"of":[20,38,46,66,72,117,128,192,203,218],"deep":[21,49,86,212],"learning":[22,50,87,213],"method,":[23,95,112,173],"its":[24],"application":[25],"in":[26,52,59,139,188],"detection":[29,41,54,81,98,150,166],"increasingly":[31],"extensive.":[32],"The":[33,143,196],"current":[34],"limitations":[35],"shortcomings":[37],"existing":[39],"methods":[42],"feasibility":[45],"improving":[47],"method":[51,77,185,206,214],"are":[55],"presented":[56],"discussed":[58],"this":[60],"paper.":[61],"According":[62],"characteristics":[65],"numerous":[67],"parts":[68],"complex":[70,118],"types":[71],"chassis":[73,79,129,164,177,219],"components,":[75],"for":[78],"identification":[83],"based":[84],"on":[85],"component":[88,109,144,154],"segmentation":[89,111],"proposed.":[91],"proposed":[94,172,205],"first":[100],"performed":[101],"using":[102],"Mask":[104,137],"regional":[105],"convolutional":[106],"neural":[107],"network":[108],"instance":[110],"which":[113,133],"reduces":[114],"influence":[116],"illumination":[119],"conditions":[120],"background":[122],"detection.":[123,221],"Next,":[124],"standard":[126],"dictionary":[127],"built,":[132],"connected":[135],"R-CNN":[138],"cascading":[141],"way.":[142],"mask":[145],"obtained":[147],"through":[148],"result,":[151],"category":[155],"information":[159],"extracted":[161],"realize":[163],"identification.":[168],"To":[169],"evaluate":[170],"an":[174],"industrial":[175,193],"was":[178],"used":[179],"create":[181],"datasets,":[182],"effective":[187],"limited":[189],"data":[190],"sets":[191],"chassis.":[195],"experimental":[197],"results":[198],"indicate":[199],"that":[200],"accuracy":[202],"can":[207],"reach":[208],"93.7%.":[209],"Overall,":[210],"realizes":[215],"complete":[216],"automation":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
