{"id":"https://openalex.org/W4416250312","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228812","title":"PMM: Post-Min-Max Augmentation for Semantic Segmentation of Underground Parking Lots","display_name":"PMM: Post-Min-Max Augmentation for Semantic Segmentation of Underground Parking Lots","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250312","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228812"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228812","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":"conference-paper","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/A5100339216","display_name":"Ye Li","orcid":"https://orcid.org/0000-0002-2898-5345"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Li","raw_affiliation_strings":["Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010110834","display_name":"Dekun Lin","orcid":"https://orcid.org/0009-0004-1800-287X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dekun Lin","raw_affiliation_strings":["Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076972480","display_name":"Zhe Cui","orcid":"https://orcid.org/0000-0002-8398-3537"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Cui","raw_affiliation_strings":["Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109186228","display_name":"Xiaolin Qin","orcid":"https://orcid.org/0000-0001-5087-8178"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolin Qin","raw_affiliation_strings":["Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Chengdu Institute of Computer Applications,Chengdu,PR China,610213","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I19820366"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46203789,"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/T12546","display_name":"Smart Parking Systems Research","score":0.6632999777793884,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12546","display_name":"Smart Parking Systems Research","score":0.6632999777793884,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.1151999980211258,"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":0.06939999759197235,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7336999773979187},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6815000176429749},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41510000824928284},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3463999927043915},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.3384000062942505},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.33489999175071716},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.31679999828338623}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7336999773979187},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7049000263214111},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6815000176429749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49950000643730164},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41510000824928284},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3463999927043915},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3206999897956848},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3160000145435333},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C2777427512","wikidata":"https://www.wikidata.org/wiki/Q6501349","display_name":"Parking lot","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.29739999771118164},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2736999988555908},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228812","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228812","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/F4320321133","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2531409750","https://openalex.org/W2560023338","https://openalex.org/W2787091153","https://openalex.org/W2886934227","https://openalex.org/W2916798096","https://openalex.org/W2955058313","https://openalex.org/W2963091558","https://openalex.org/W2963125010","https://openalex.org/W2963419596","https://openalex.org/W2963516811","https://openalex.org/W2963890956","https://openalex.org/W2964217532","https://openalex.org/W2981689412","https://openalex.org/W3014641072","https://openalex.org/W3159637683","https://openalex.org/W3169865585","https://openalex.org/W3196904463","https://openalex.org/W4312688875","https://openalex.org/W4312785900","https://openalex.org/W4386076267","https://openalex.org/W4402716243","https://openalex.org/W4403941610"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,21,43,58,64,95,104,113,123,126,133,143,159,164,167,180,200,216],"rapid":[2],"development":[3],"of":[4,39,66,125,145,166,220],"autonomous":[5,221],"driving":[6],"technology,":[7],"automated":[8,234],"valet":[9,235],"parking":[10,24,78,225,236],"has":[11,118],"imposed":[12],"more":[13],"stringent":[14],"requirements":[15],"on":[16,94,111,208],"environmental":[17,40],"perception,":[18,41],"especially":[19],"in":[20,57,70,76,107,137,190,223],"demanding":[22],"underground":[23,77,224],"scenarios":[25],"featuring":[26],"complex":[27],"structures":[28],"and":[29,80,88,141,194],"diverse":[30],"lighting":[31],"conditions.":[32],"Semantic":[33],"segmentation,":[34,72],"as":[35,149],"a":[36],"pivotal":[37],"part":[38],"enables":[42],"vehicle":[44],"to":[45,54,203],"better":[46],"understand":[47],"its":[48],"surroundings":[49],"by":[50,157],"assigning":[51],"semantic":[52,71,191,217],"labels":[53],"every":[55],"pixel":[56],"image.":[59],"This":[60,211],"study":[61],"delves":[62],"into":[63],"application":[65],"multi-scale":[67,169,185],"augmentation":[68,87,116],"techniques":[69],"pinpoints":[73],"their":[74,206],"limitations":[75],"scenarios,":[79],"puts":[81],"forward":[82],"two":[83,127],"novel":[84,129],"strategies:":[85],"post-maximum":[86],"post-minimum":[89],"augmentation.":[90],"Experiments":[91],"carried":[92],"out":[93],"AVM-SemSeg":[96],"dataset":[97],"validate":[98],"that":[99,179],"these":[100],"strategies":[101],"remarkably":[102],"boost":[103],"model\u2019s":[105,134,168],"performance":[106,207],"such":[108],"scenarios.":[109],"Based":[110],"this,":[112],"post-min-max":[114],"(PMM)":[115],"method":[117,182],"been":[119],"further":[120],"developed,":[121],"integrating":[122],"advantages":[124],"aforementioned":[128],"strategies.":[130],"It":[131],"elevates":[132],"predictive":[135],"confidence":[136],"true":[138],"positive":[139,147],"samples":[140],"curbs":[142],"incidence":[144],"false":[146],"misclassifications":[148],"background.":[150],"The":[151,176],"computational":[152],"burden":[153],"is":[154],"also":[155,228],"alleviated":[156],"reducing":[158],"scale":[160],"inputs,":[161],"which":[162],"enhances":[163],"efficiency":[165],"inference":[170,186],"while":[171],"still":[172],"maintaining":[173],"competitive":[174],"results.":[175],"results":[177],"manifest":[178],"PMM":[181],"outperforms":[183],"existing":[184],"information":[187],"fusion":[188],"methods":[189,202],"segmentation":[192,218],"tasks":[193],"can":[195],"be":[196],"combined":[197],"with":[198],"all":[199],"tested":[201],"notably":[204],"enhance":[205],"this":[209],"dataset.":[210],"research":[212],"not":[213],"only":[214],"augments":[215],"capabilities":[219],"vehicles":[222],"environments":[226],"but":[227],"offers":[229],"vital":[230],"technical":[231],"support":[232],"for":[233],"tasks.":[237]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-14T00:00:00"}
