{"id":"https://openalex.org/W4366380929","doi":"https://doi.org/10.1145/3584376.3584543","title":"Lightweight steel pipe counting study based on improved MobileCount","display_name":"Lightweight steel pipe counting study based on improved MobileCount","publication_year":2022,"publication_date":"2022-12-16","ids":{"openalex":"https://openalex.org/W4366380929","doi":"https://doi.org/10.1145/3584376.3584543"},"language":"en","primary_location":{"id":"doi:10.1145/3584376.3584543","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584376.3584543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","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/A5089861336","display_name":"Kaiwei Deng","orcid":"https://orcid.org/0000-0001-6724-6070"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaiwei Deng","raw_affiliation_strings":["Wuhan Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075768320","display_name":"Lizhi Liu","orcid":"https://orcid.org/0000-0002-5093-0316"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhi Liu","raw_affiliation_strings":["Wuhan Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology, China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060311479","display_name":"Ting Liu","orcid":"https://orcid.org/0000-0001-8016-9858"},"institutions":[{"id":"https://openalex.org/I4210124300","display_name":"Hubei Provincial Women and Children's Hospital","ror":"https://ror.org/03323pz98","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210124300"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Liu","raw_affiliation_strings":["Hubei Women's Cadre School, China"],"affiliations":[{"raw_affiliation_string":"Hubei Women's Cadre School, China","institution_ids":["https://openalex.org/I4210124300"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047050490","display_name":"Jiatong Ju","orcid":"https://orcid.org/0000-0002-7159-0007"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiatong Ju","raw_affiliation_strings":["Wuhan Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology, China","institution_ids":["https://openalex.org/I91125648"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5089861336"],"corresponding_institution_ids":["https://openalex.org/I91125648"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18812972,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"934","last_page":"939"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9858999848365784,"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"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9858999848365784,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9531000256538391,"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/T13647","display_name":"AI and Big Data Applications","score":0.9502000212669373,"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/upsampling","display_name":"Upsampling","score":0.8603540658950806},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6613002419471741},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.553808331489563},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5316131114959717},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5039369463920593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4278028607368469},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32176631689071655},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19341546297073364}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8603540658950806},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6613002419471741},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.553808331489563},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5316131114959717},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5039369463920593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4278028607368469},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32176631689071655},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19341546297073364},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3584376.3584543","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3584376.3584543","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2088929512","https://openalex.org/W2895051362","https://openalex.org/W4297976725"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W1983892167","https://openalex.org/W2281134365","https://openalex.org/W4310746709","https://openalex.org/W4385574037","https://openalex.org/W4212888438","https://openalex.org/W4388321867"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,13,48,54,106,114,127],"time-consuming":[3],"and":[4,20,68,103,126,139],"laborious":[5],"problem":[6],"of":[7,23,121,124,145],"manpower":[8],"counting":[9,22,30,110,146],"steel":[10,24,28,108],"pipes":[11],"in":[12,113,151],"current":[14],"construction":[15],"industry,":[16],"to":[17,52,73,90],"achieve":[18],"rapid":[19],"accurate":[21],"pipes,":[25],"a":[26,41,70],"lightweight":[27,42,71],"pipe":[29,109],"network":[31,51,60,111],"STNet":[32],"based":[33],"on":[34,81],"improved":[35],"MobileCount":[36,104],"is":[37,45,130,134],"proposed.":[38],"MobileNetv3":[39],"with":[40,100],"attention":[43],"mechanism":[44],"used":[46],"as":[47],"model":[49,128],"backbone":[50],"enhance":[53],"feature":[55],"extraction":[56],"ability.":[57],"The":[58,94,143],"back-end":[59],"avoids":[61],"information":[62],"loss":[63],"caused":[64],"by":[65],"multiple":[66],"upsampling":[67],"uses":[69],"decoder":[72],"output":[74],"an":[75,119],"8-fold":[76],"downsampling":[77],"prediction":[78],"map.":[79],"Based":[80],"migration":[82],"Learning":[83],"ideas,":[84],"using":[85],"pre-trained":[86],"weight":[87,129],"training":[88],"models":[89,138],"conduct":[91],"comparative":[92],"experiments.":[93],"experimental":[95],"results":[96],"show":[97],"that":[98],"compared":[99],"CANNet,":[101],"CSRNet,":[102],"network,":[105],"proposed":[107],"STNet,":[112],"self-made":[115],"data":[116],"set,":[117],"has":[118],"MAE":[120],"9.14,":[122],"MSE":[123],"11.40,":[125],"only":[131],"3.6M,":[132],"which":[133],"better":[135,141],"than":[136],"other":[137],"achieves":[140],"results.":[142],"effect":[144],"can":[147],"be":[148],"effectively":[149],"applied":[150],"production":[152],"practice.":[153]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}