{"id":"https://openalex.org/W4416249854","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228124","title":"BSP-YOLOv10: A Small Object Detection Model for Detecting Appearance Scratches in Barrel-Shaped Products","display_name":"BSP-YOLOv10: A Small Object Detection Model for Detecting Appearance Scratches in Barrel-Shaped Products","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249854","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228124"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5047198497","display_name":"Chao Ye","orcid":"https://orcid.org/0000-0003-1473-7575"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Ye","raw_affiliation_strings":["Soochow University,School of Computer Science and Technology,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"Soochow University,School of Computer Science and Technology,Suzhou,China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014261323","display_name":"Wei Chen","orcid":"https://orcid.org/0000-0002-5382-0542"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Chen","raw_affiliation_strings":["Soochow University,School of Computer Science and Technology,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"Soochow University,School of Computer Science and Technology,Suzhou,China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102005569","display_name":"Lei Zhao","orcid":"https://orcid.org/0000-0001-9077-5720"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhao","raw_affiliation_strings":["Soochow University,School of Computer Science and Technology,Suzhou,China"],"affiliations":[{"raw_affiliation_string":"Soochow University,School of Computer Science and Technology,Suzhou,China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047198497"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34273469,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.7024999856948853,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7024999856948853,"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":0.17720000445842743,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.0066999997943639755,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/object-detection","display_name":"Object detection","score":0.8029000163078308},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6319000124931335},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5457000136375427},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5266000032424927},{"id":"https://openalex.org/keywords/machine-vision","display_name":"Machine vision","score":0.5099999904632568},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.4805999994277954},{"id":"https://openalex.org/keywords/object-class-detection","display_name":"Object-class detection","score":0.42570000886917114},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.4115999937057495},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4077000021934509}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8414999842643738},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.8029000163078308},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7908999919891357},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6959999799728394},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6319000124931335},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5457000136375427},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5266000032424927},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.5099999904632568},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.42570000886917114},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.4115999937057495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.4016000032424927},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3686999976634979},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35830000042915344},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C146920229","wikidata":"https://www.wikidata.org/wiki/Q2278114","display_name":"Automated X-ray inspection","level":4,"score":0.3431999981403351},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.33739998936653137},{"id":"https://openalex.org/C2777709985","wikidata":"https://www.wikidata.org/wiki/Q770135","display_name":"Conveyor belt","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.32989999651908875},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2635999917984009},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228124","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2601564443","https://openalex.org/W2886335102","https://openalex.org/W2901931186","https://openalex.org/W2963037989","https://openalex.org/W2963351448","https://openalex.org/W2963857746","https://openalex.org/W2982363097","https://openalex.org/W2982770724","https://openalex.org/W2988887648","https://openalex.org/W2989604896","https://openalex.org/W3024889357","https://openalex.org/W3034971973","https://openalex.org/W3159196909","https://openalex.org/W3161063006","https://openalex.org/W3165012668","https://openalex.org/W3172087149","https://openalex.org/W3194961735","https://openalex.org/W4206913987","https://openalex.org/W4312141633","https://openalex.org/W4312823573","https://openalex.org/W4367665525","https://openalex.org/W4382568144","https://openalex.org/W4386076325","https://openalex.org/W4386220223","https://openalex.org/W4402716047","https://openalex.org/W4403770406"],"related_works":[],"abstract_inverted_index":{"Industrial":[0],"vision":[1,12,164],"inspection":[2,13,54,67,73,165],"relies":[3],"on":[4,174,256],"image":[5,86],"acquisition,":[6],"processing,":[7],"and":[8,36,69,87,91,154,198,217,241,259,264],"analysis.":[9],"A":[10],"common":[11],"method":[14,48],"used":[15],"in":[16,84,135,209,233,253],"industry":[17],"is":[18],"to":[19,28,41,55,132,147,188],"install":[20],"an":[21,103],"industrial":[22],"camera":[23],"above":[24],"the":[25,30,34,38,50,63,77,82,85,89,94,122,130,141,177,180,192,195,199,210,215,229,236,248],"conveyor":[26],"belt":[27,35],"photograph":[29],"products":[31,258],"passing":[32],"through":[33],"classify":[37],"resulting":[39],"images":[40],"determine":[42],"their":[43],"defect":[44,108],"categories.":[45],"Although":[46],"this":[47],"improves":[49,214,235,261],"automation":[51],"level":[52],"of":[53,65,72,93,124,194,219],"a":[56,97,113,162,204],"certain":[57],"extent,":[58],"it":[59],"also":[60],"suffers":[61],"from":[62],"problems":[64],"incomplete":[66],"perspective":[68],"low":[70,151],"accuracy":[71,153,216,263],"results.":[74],"To":[75,156],"overcome":[76],"problem,":[78],"existing":[79],"studies":[80],"localize":[81],"object":[83,95,104,119,206],"mark":[88],"location":[90],"category":[92],"with":[96,107,150],"detection":[98,105,109,120,152,171,207,211,262],"frame.":[99],"This":[100],"approach":[101],"trains":[102],"model":[106,131,172,178,250],"capability":[110],"by":[111],"labeling":[112],"large":[114],"defective":[115],"dataset.":[116],"However,":[117],"small":[118,205,221],"faces":[121],"problem":[123],"fewer":[125],"features,":[126],"which":[127,190,213,232],"may":[128],"cause":[129],"have":[133],"difficulty":[134],"capturing":[136],"enough":[137],"discriminative":[138],"information":[139],"during":[140],"learning":[142],"process":[143],"thus":[144],"being":[145],"prone":[146],"overfitting,":[148],"often":[149],"confidence.":[155],"address":[157],"these":[158],"challenges,":[159],"we":[160],"propose":[161],"new":[163],"model,":[166],"BSP-YOLOv10":[167,249],"(a":[168],"Barrel-Shaped":[169],"Products":[170],"based":[173],"YOLOv10).":[175],"Specifically,":[176],"contains":[179],"following":[181],"three":[182],"innovative":[183],"parts:":[184],"(1)":[185],"applying":[186],"NWDLoss":[187],"YOLOv10,":[189],"realizes":[191],"combination":[193],"Wasserstein":[196],"loss":[197],"IoU":[200],"loss;":[201],"(2)":[202],"adding":[203],"layer":[208],"phase,":[212],"efficiency":[218,240],"detecting":[220,254],"objects;":[222],"(3)":[223],"improving":[224],"DSConv":[225],"module":[226],"thereby":[227],"reducing":[228],"computation":[230],"amount,":[231],"turn":[234],"convolutional":[237],"layer\u2019s":[238],"memory":[239],"speed.":[242],"The":[243],"experimental":[244],"results":[245],"demonstrate":[246],"that":[247],"performs":[251],"well":[252],"scratches":[255],"barrel-shaped":[257],"effectively":[260],"efficiency.":[265]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
