{"id":"https://openalex.org/W7160172069","doi":"https://doi.org/10.1016/j.engappai.2026.114973","title":"Research on defect recognition method of mine belts based on deep learning","display_name":"Research on defect recognition method of mine belts based on deep learning","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160172069","doi":"https://doi.org/10.1016/j.engappai.2026.114973"},"language":"en","primary_location":{"id":"doi:10.1016/j.engappai.2026.114973","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114973","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.engappai.2026.114973","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135125270","display_name":"Jingyi Liu","orcid":null},"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":"Jingyi Liu","raw_affiliation_strings":["College of Sciences, Northeastern University, Shenyang, 110819, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Sciences, Northeastern University, Shenyang, 110819, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135121153","display_name":"Hanquan Zhang","orcid":"https://orcid.org/0009-0009-4121-1465"},"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":true,"raw_author_name":"Hanquan Zhang","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China"],"raw_orcid":"https://orcid.org/0009-0009-4121-1465","affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135238207","display_name":"Dong Xiao","orcid":null},"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":"Dong Xiao","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5135121153"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":{"value":3170,"currency":"USD","value_usd":3170},"apc_paid":{"value":3170,"currency":"USD","value_usd":3170},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.47831844,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"177","issue":null,"first_page":"114973","last_page":"114973"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13341","display_name":"Belt Conveyor Systems Engineering","score":0.32580000162124634,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T13341","display_name":"Belt Conveyor Systems Engineering","score":0.32580000162124634,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T12282","display_name":"Mineral Processing and Grinding","score":0.08550000190734863,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T14392","display_name":"Geoscience and Mining Technology","score":0.041600000113248825,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/deep-learning","display_name":"Deep learning","score":0.8140000104904175},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.3319000005722046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3158000111579895},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30399999022483826},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.30390000343322754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8374000191688538},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.8140000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.707099974155426},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4034000039100647},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3319000005722046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3172999918460846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.301800012588501}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.engappai.2026.114973","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114973","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.engappai.2026.114973","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.engappai.2026.114973","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.41375717520713806}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1992189566","https://openalex.org/W3132036977","https://openalex.org/W3187693437","https://openalex.org/W3194599279","https://openalex.org/W4229082353","https://openalex.org/W4288045939","https://openalex.org/W4295832403","https://openalex.org/W4391697092","https://openalex.org/W4409129147","https://openalex.org/W4413126756"],"related_works":[],"abstract_inverted_index":{"The":[0,175],"traditional":[1],"manual":[2],"inspection":[3],"method":[4,45,168],"is":[5,98,104,150],"inefficient":[6],"and":[7,21,34,67,73,84,117,127,131,142,160],"prone":[8],"to":[9,32,64],"missed":[10],"detections.":[11],"Although":[12],"the":[13,55,77,81,88,129,134,139,143,154,164,167,180,187],"existing":[14],"non-destructive":[15],"testing":[16],"technologies":[17],"such":[18],"as":[19],"ultrasonic":[20],"infrared":[22],"imaging":[23],"equipment":[24,37],"can":[25],"provide":[26],"accurate":[27],"results,":[28],"they":[29],"are":[30],"complex":[31,115,137],"operate":[33],"have":[35],"high":[36,120],"costs.":[38],"This":[39],"paper":[40],"proposes":[41],"a":[42],"defect":[43,107],"recognition":[44,102],"for":[46,106],"mining":[47],"belts":[48],"based":[49],"on":[50],"deep":[51],"learning,":[52],"which":[53],"uses":[54],"You":[56,89],"Only":[57,90],"Look":[58,91],"Once":[59,92],"(YOLO)":[60],"object":[61],"detection":[62],"algorithm":[63],"automatically":[65],"identify":[66],"classify":[68],"surface":[69],"cracks,":[70],"wear,":[71],"tears":[72],"other":[74],"defects":[75],"of":[76,87,133,166,195],"belts.":[78],"By":[79],"analyzing":[80],"network":[82],"structure":[83],"experimental":[85,176],"results":[86,177],"version":[93],"8":[94],"(YOLOv8)":[95],"algorithm,":[96],"it":[97],"found":[99],"that":[100,179],"its":[101],"accuracy":[103,132,185],"poor":[105],"categories":[108],"with":[109,114,119],"small":[110],"sample":[111],"sizes,":[112],"samples":[113,118],"environments,":[116,138],"noise":[121],"levels.":[122],"To":[123],"solve":[124],"these":[125],"problems":[126],"improve":[128],"robustness":[130],"model":[135,182,189],"in":[136,190],"attention":[140],"mechanism":[141],"Focal":[144,161],"Loss":[145],"function":[146],"were":[147],"introduced.":[148],"YOLOv8":[149],"improved":[151,181],"by":[152],"introducing":[153],"Convolutional":[155],"Block":[156],"Attention":[157],"Module":[158],"(CBAM)":[159],"Loss.":[162],"Finally,":[163],"effectiveness":[165],"we":[169],"proposed":[170],"was":[171],"verified":[172],"through":[173],"experiments.":[174],"show":[178],"demonstrates":[183],"better":[184],"than":[186],"original":[188],"identifying":[191],"all":[192],"five":[193],"types":[194],"defects.":[196]},"counts_by_year":[],"updated_date":"2026-06-18T08:10:14.011955","created_date":"2026-05-05T00:00:00"}