{"id":"https://openalex.org/W4400770576","doi":"https://doi.org/10.1109/access.2024.3430931","title":"Semantic Image Segmentation Based on Superpixel-Pooling DCNN With Random Walks on Graph and Application to Intrusion Detection of Railway","display_name":"Semantic Image Segmentation Based on Superpixel-Pooling DCNN With Random Walks on Graph and Application to Intrusion Detection of Railway","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400770576","doi":"https://doi.org/10.1109/access.2024.3430931"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3430931","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3430931","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3430931","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wang Fuzhi","orcid":"https://orcid.org/0000-0003-2274-779X"},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Fuzhi","raw_affiliation_strings":["School of Mechanical Engineering, Xihua University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-2274-779X","affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Xihua University, Chengdu, China","institution_ids":["https://openalex.org/I102345215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021792103","display_name":"Changlin Song","orcid":null},"institutions":[{"id":"https://openalex.org/I102345215","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26","country_code":"CN","type":"education","lineage":["https://openalex.org/I102345215"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Changlin","raw_affiliation_strings":["School of Mechanical Engineering, Xihua University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical Engineering, Xihua University, Chengdu, China","institution_ids":["https://openalex.org/I102345215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102345215"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11753207,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"100086","last_page":"100101"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9965000152587891,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9646999835968018,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.7467941641807556},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7116509079933167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6629605293273926},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.62295001745224},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5808408856391907},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5804620981216431},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5114092230796814},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5059186816215515},{"id":"https://openalex.org/keywords/random-walk","display_name":"Random walk","score":0.5017609596252441},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47938230633735657},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4671682119369507},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4446495473384857},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4357137084007263},{"id":"https://openalex.org/keywords/random-walker-algorithm","display_name":"Random walker algorithm","score":0.4238327145576477},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37065619230270386},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20477235317230225},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10872998833656311}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7467941641807556},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7116509079933167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6629605293273926},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.62295001745224},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5808408856391907},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5804620981216431},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5114092230796814},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5059186816215515},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.5017609596252441},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47938230633735657},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4671682119369507},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4446495473384857},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4357137084007263},{"id":"https://openalex.org/C105045736","wikidata":"https://www.wikidata.org/wiki/Q7292002","display_name":"Random walker algorithm","level":3,"score":0.4238327145576477},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37065619230270386},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20477235317230225},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10872998833656311},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3430931","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3430931","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c73485211c2248e5830aace0eb7374ce","is_oa":true,"landing_page_url":"https://doaj.org/article/c73485211c2248e5830aace0eb7374ce","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 100086-100101 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3430931","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3430931","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1215024616","display_name":null,"funder_award_id":"Z1120223","funder_id":"https://openalex.org/F4320324858","funder_display_name":"Xihua University"}],"funders":[{"id":"https://openalex.org/F4320324858","display_name":"Xihua University","ror":"https://ror.org/04gwtvf26"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W2088049833","https://openalex.org/W2125637308","https://openalex.org/W2412782625","https://openalex.org/W2508741746","https://openalex.org/W2592370397","https://openalex.org/W2605214291","https://openalex.org/W2630837129","https://openalex.org/W2793870514","https://openalex.org/W2807005761","https://openalex.org/W2810392541","https://openalex.org/W2889487566","https://openalex.org/W2911045448","https://openalex.org/W2912327653","https://openalex.org/W2946315763","https://openalex.org/W2955058313","https://openalex.org/W2963727650","https://openalex.org/W2964309882","https://openalex.org/W2964333009","https://openalex.org/W2968114279","https://openalex.org/W2987852271","https://openalex.org/W3035241330","https://openalex.org/W3087444718","https://openalex.org/W3105871679","https://openalex.org/W3107113572","https://openalex.org/W3111279179","https://openalex.org/W3118695686","https://openalex.org/W3134571421","https://openalex.org/W3161994160","https://openalex.org/W3194619855","https://openalex.org/W3208409956","https://openalex.org/W3212133955","https://openalex.org/W4205163225","https://openalex.org/W4210719005","https://openalex.org/W4283517888","https://openalex.org/W4386076267","https://openalex.org/W6640295612","https://openalex.org/W6739696289","https://openalex.org/W6752224571"],"related_works":["https://openalex.org/W1973641485","https://openalex.org/W2472745029","https://openalex.org/W2000620996","https://openalex.org/W2064285815","https://openalex.org/W3171278913","https://openalex.org/W2793605182","https://openalex.org/W3163695673","https://openalex.org/W4287163214","https://openalex.org/W2900071037","https://openalex.org/W2760154051"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks(DCNN)":[3],"has":[4],"recently":[5],"significantly":[6],"pushed":[7],"the":[8,11,19,23,42,90,100,112,121,146,164,180,183],"state":[9],"of":[10,13,31,38,60,103,120,182],"art":[12],"semantic":[14,78,171],"image":[15,43,107,113,172],"segmentation.":[16,173],"However,":[17],"as":[18,57,99,152],"network":[20,130],"depth":[21],"increases,":[22],"progressively":[24],"lower":[25],"resolution":[26],"leads":[27],"to":[28,105,162,167,193],"a":[29,35,77,127],"loss":[30],"feature":[32,114],"information":[33,109],"and":[34,85,110,141,155,188],"large":[36],"number":[37],"segmentation":[39,55,79,123],"errors":[40],"near":[41],"edges.":[44],"This":[45],"is":[46,97,160],"an":[47],"imperative":[48],"problem":[49],"for":[50],"applications":[51],"that":[52,81],"require":[53],"accurate":[54],"such":[56],"intrusion":[58,195],"detection":[59],"railway.":[61],"To":[62],"address":[63],"this":[64,186],"problem,":[65],"we":[66],"propose":[67],"superpixel":[68],"pooling":[69],"DCNN":[70],"combined":[71],"with":[72,93],"random":[73,86,157],"walks":[74,87],"networks":[75],"(SDR-Net),":[76],"model":[80],"combines":[82],"improved":[83,91],"DeepLabV3+":[84,92],"algorithm.":[88],"Firstly,":[89],"superpixel-pooling":[94],"ASPP":[95],"(SPASPP)":[96],"used":[98,151],"basic":[101],"structure":[102],"SDR-Net":[104],"introduce":[106],"prior":[108],"improve":[111],"extraction":[115],"performance.":[116],"Then,":[117],"all":[118],"pixels":[119,140,149,166],"initial":[122],"are":[124,150],"classified":[125],"by":[126],"binary":[128],"classification":[129],"based":[131],"on":[132,175],"D2S":[133],"transformation":[134],"into":[135],"two":[136],"categories:":[137],"correctly":[138,147],"labeled":[139,143,148],"incorrectly":[142],"pixels.":[144],"Thirdly,":[145],"seed":[153],"points,":[154],"then":[156],"walk":[158],"algorithm":[159,184],"applied":[161,192],"reclassify":[163],"mislabeled":[165],"achieve":[168],"higher":[169],"precision":[170],"Experiments":[174],"PASCAL":[176],"VOC2012":[177],"datasets":[178],"show":[179],"effectiveness":[181],"in":[185],"paper,":[187],"it":[189],"was":[190],"successfully":[191],"rail":[194],"detection.":[196]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}