{"id":"https://openalex.org/W3091506353","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207577","title":"Machine Vision for Construction Equipment by Transfer Learning with Scale Models","display_name":"Machine Vision for Construction Equipment by Transfer Learning with Scale Models","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091506353","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207577","mag":"3091506353"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5013524200","display_name":"Carl Borngrund","orcid":"https://orcid.org/0000-0002-4716-9765"},"institutions":[{"id":"https://openalex.org/I190632392","display_name":"Lule\u00e5 University of Technology","ror":"https://ror.org/016st3p78","country_code":"SE","type":"education","lineage":["https://openalex.org/I190632392"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Carl Borngrund","raw_affiliation_strings":["Embedded Intelligent Systems Lab (EISLAB), Lulea University of Technology, Lule\u00e5, Sweden"],"affiliations":[{"raw_affiliation_string":"Embedded Intelligent Systems Lab (EISLAB), Lulea University of Technology, Lule\u00e5, Sweden","institution_ids":["https://openalex.org/I190632392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055726181","display_name":"Ulf Bodin","orcid":"https://orcid.org/0000-0001-5408-0008"},"institutions":[{"id":"https://openalex.org/I190632392","display_name":"Lule\u00e5 University of Technology","ror":"https://ror.org/016st3p78","country_code":"SE","type":"education","lineage":["https://openalex.org/I190632392"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ulf Bodin","raw_affiliation_strings":["Embedded Intelligent Systems Lab (EISLAB), Lulea University of Technology, Lule\u00e5, Sweden"],"affiliations":[{"raw_affiliation_string":"Embedded Intelligent Systems Lab (EISLAB), Lulea University of Technology, Lule\u00e5, Sweden","institution_ids":["https://openalex.org/I190632392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072244112","display_name":"Fredrik Sandin","orcid":"https://orcid.org/0000-0001-5662-825X"},"institutions":[{"id":"https://openalex.org/I190632392","display_name":"Lule\u00e5 University of Technology","ror":"https://ror.org/016st3p78","country_code":"SE","type":"education","lineage":["https://openalex.org/I190632392"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Fredrik Sandin","raw_affiliation_strings":["Embedded Intelligent Systems Lab (EISLAB), Lulea University of Technology, Lule\u00e5, Sweden"],"affiliations":[{"raw_affiliation_string":"Embedded Intelligent Systems Lab (EISLAB), Lulea University of Technology, Lule\u00e5, Sweden","institution_ids":["https://openalex.org/I190632392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5013524200"],"corresponding_institution_ids":["https://openalex.org/I190632392"],"apc_list":null,"apc_paid":null,"fwci":0.8541,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.71690051,"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":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9975000023841858,"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":0.9975000023841858,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9919000267982483,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9610000252723694,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/truck","display_name":"Truck","score":0.8558920621871948},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7452996969223022},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.686331033706665},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5976973176002502},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5973179936408997},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5905580520629883},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5853835344314575},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5616804361343384},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5132922530174255},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4993302822113037},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4741774797439575},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.46758127212524414},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44550880789756775},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.4100243151187897},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1413927674293518},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.09164634346961975},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.08397448062896729}],"concepts":[{"id":"https://openalex.org/C52121051","wikidata":"https://www.wikidata.org/wiki/Q43193","display_name":"Truck","level":2,"score":0.8558920621871948},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7452996969223022},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.686331033706665},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5976973176002502},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5973179936408997},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5905580520629883},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5853835344314575},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5616804361343384},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5132922530174255},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4993302822113037},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4741774797439575},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.46758127212524414},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44550880789756775},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.4100243151187897},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1413927674293518},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.09164634346961975},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.08397448062896729},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207577","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5299999713897705,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W181942421","https://openalex.org/W255452181","https://openalex.org/W1861492603","https://openalex.org/W2138683543","https://openalex.org/W2146433214","https://openalex.org/W2167683317","https://openalex.org/W2194775991","https://openalex.org/W2570343428","https://openalex.org/W2615547864","https://openalex.org/W2792919579","https://openalex.org/W2796347433","https://openalex.org/W2884561390","https://openalex.org/W2912493354","https://openalex.org/W2959193261","https://openalex.org/W2962867954","https://openalex.org/W2963037989","https://openalex.org/W2963730616","https://openalex.org/W2964271185","https://openalex.org/W2970971581","https://openalex.org/W2981540341","https://openalex.org/W2990747716","https://openalex.org/W3041577572","https://openalex.org/W3046238886","https://openalex.org/W3208521716","https://openalex.org/W3209288023","https://openalex.org/W4293584584","https://openalex.org/W4295312788","https://openalex.org/W4295719664","https://openalex.org/W6737769214","https://openalex.org/W6745448612","https://openalex.org/W6745935785","https://openalex.org/W6748851097","https://openalex.org/W6750227808","https://openalex.org/W6766978945","https://openalex.org/W6770858630","https://openalex.org/W6780679491","https://openalex.org/W6802894125"],"related_works":["https://openalex.org/W1517019597","https://openalex.org/W1968776045","https://openalex.org/W2296713838","https://openalex.org/W767149399","https://openalex.org/W3036261569","https://openalex.org/W2889950528","https://openalex.org/W575062473","https://openalex.org/W4297672583","https://openalex.org/W2578444090","https://openalex.org/W2963548962"],"abstract_inverted_index":{"Machine":[0],"vision":[1],"is":[2,66,98,152],"required":[3],"by":[4],"autonomous":[5],"heavy":[6,78],"construction":[7,69,96],"equipment":[8,27,79],"to":[9,21,28,131,188,225],"navigate":[10],"and":[11,25,35,120,126,169,192,214,243],"interact":[12],"with":[13,233],"the":[14,19,30,55,73,112,183,189,193,201,204],"environment.":[15],"Wheel":[16],"loaders":[17],"need":[18],"ability":[20],"identify":[22],"different":[23,123],"objects":[24,227],"other":[26],"perform":[29],"task":[31],"of":[32,58,62,72,75,114,141,146,160,203,210,239],"automatically":[33],"loading":[34],"dumping":[36],"material":[37],"on":[38,182,228],"dump":[39,150,165,206,231],"trucks,":[40],"which":[41,65,86],"can":[42,90,104,199,220],"be":[43,91,105],"achieved":[44],"using":[45,115],"deep":[46],"neural":[47,88],"networks.":[48],"Training":[49],"such":[50],"networks":[51,89,125,198,219],"from":[52],"scratch":[53],"requires":[54],"iterative":[56],"collection":[57],"potentially":[59],"large":[60],"amounts":[61],"video":[63],"data,":[64],"challenging":[67],"at":[68,95],"sites":[70],"because":[71],"complexity":[74],"safely":[76],"operating":[77],"in":[80],"realistic":[81],"environments.":[82],"Transfer":[83],"learning,":[84],"for":[85,93,118,212,216,241,245],"pretrained":[87,124,181],"retrained":[92],"use":[94,127],"sites,":[97],"thus":[99],"attractive,":[100],"especially":[101],"if":[102],"data":[103,117,130],"acquired":[106],"without":[107],"full-scale":[108],"experiments.":[109],"We":[110],"investigate":[111],"possibility":[113],"scalemodel":[116],"training":[119,236],"validating":[121],"two":[122],"real-world":[128],"test":[129,158],"examine":[132],"their":[133],"generalization":[134],"capability.":[135],"A":[136],"dataset":[137,170],"containing":[138],"268":[139],"images":[140,159],"a":[142,147,161,229],"1:16":[143],"scale":[144],"model":[145],"Volvo":[148,163],"A60H":[149],"truck":[151,207,232],"provided,":[153],"as":[154,156],"well":[155],"64":[157],"full-size":[162,230],"A25G":[164],"truck.":[166],"The":[167,178],"code":[168],"are":[171],"publicly":[172],"available":[173],"<sup":[174],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[175],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[176],".":[177],"networks,":[179],"both":[180,197],"MS-COCO":[184],"dataset,":[185,191],"were":[186],"fine-tuned":[187],"created":[190],"results":[194],"indicate":[195],"that":[196],"learn":[200],"features":[202,224],"scale-model":[205],"(validation":[208],"mAP":[209,238],"0.82":[211],"YOLOv3":[213,242],"0.95":[215],"RetinaNet).":[217,246],"Both":[218],"transfer":[221],"these":[222],"learned":[223],"detect":[226],"no":[234],"additional":[235],"(test":[237],"0.70":[240],"0.79":[244]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
