{"id":"https://openalex.org/W3134478858","doi":"https://doi.org/10.1145/3446132.3446145","title":"Lane Detection Combining Details and Integrity: an Advanced Method for Lane Detection","display_name":"Lane Detection Combining Details and Integrity: an Advanced Method for Lane Detection","publication_year":2020,"publication_date":"2020-12-24","ids":{"openalex":"https://openalex.org/W3134478858","doi":"https://doi.org/10.1145/3446132.3446145","mag":"3134478858"},"language":"en","primary_location":{"id":"doi:10.1145/3446132.3446145","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3446132.3446145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","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/A5106415218","display_name":"Xingjian Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xingjian Dai","raw_affiliation_strings":["Nanjing University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089409762","display_name":"Jin Xie","orcid":"https://orcid.org/0000-0001-6978-8834"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Xie","raw_affiliation_strings":["Nanjing University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064363522","display_name":"Jianjun Qian","orcid":"https://orcid.org/0000-0002-0968-8556"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianjun Qian","raw_affiliation_strings":["Nanjing University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100726984","display_name":"Jian Yang","orcid":"https://orcid.org/0000-0003-4800-832X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yang","raw_affiliation_strings":["Nanjing University of Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5106415218"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.1007,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49431571,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T12549","display_name":"Image and Object Detection Techniques","score":0.9733999967575073,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9610000252723694,"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/computer-science","display_name":"Computer science","score":0.8580946922302246},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7171123027801514},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7097058892250061},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6704062223434448},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5467410087585449},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4974992573261261},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4965527653694153},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.47910788655281067},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4661518335342407},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3879563808441162},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36163949966430664}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8580946922302246},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7171123027801514},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7097058892250061},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6704062223434448},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5467410087585449},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4974992573261261},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4965527653694153},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.47910788655281067},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4661518335342407},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3879563808441162},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36163949966430664},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3446132.3446145","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3446132.3446145","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W2080553371","https://openalex.org/W2118545852","https://openalex.org/W2125022378","https://openalex.org/W2146575011","https://openalex.org/W2170178366","https://openalex.org/W2194775991","https://openalex.org/W2549139847","https://openalex.org/W2567320498","https://openalex.org/W2762439315","https://openalex.org/W2780740184","https://openalex.org/W2913960518","https://openalex.org/W2964332990","https://openalex.org/W2971079005","https://openalex.org/W2981441441","https://openalex.org/W3034985293","https://openalex.org/W3102168793","https://openalex.org/W3109790059","https://openalex.org/W3120688202","https://openalex.org/W4230536567"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Lane":[0,58],"detection":[1,20],"methods":[2,75,88,106,196],"based":[3,159],"on":[4,160,185],"convolutional":[5],"neural":[6],"network":[7],"have":[8,117],"achieved":[9],"excellent":[10],"performance":[11],"in":[12,107,132,141],"recent":[13],"years.":[14],"Most":[15],"of":[16,39,42,71,103,121,149,174],"them":[17],"treat":[18],"lane":[19,43,108,123,150],"as":[21,89,162,178],"a":[22,33,118,154],"semantic":[23],"segmentation":[24],"task":[25],"which":[26,65,166],"judges":[27],"whether":[28],"each":[29],"pixel":[30],"belongs":[31],"to":[32,48,116,170,179],"lane.":[34,52],"To":[35,144],"make":[36],"full":[37],"use":[38],"the":[40,50,69,73,77,90,94,101,104,114,122,126,133,142,147,163,171,175,186,199],"characteristics":[41],"shape,":[44],"some":[45],"researchers":[46],"proposed":[47],"predict":[49],"whole":[51],"In":[53],"this":[54],"paper,":[55],"we":[56,81,152],"propose":[57],"Detection":[59],"Combining":[60],"Details":[61],"and":[62,76,135,197],"Integrity":[63],"(LDCDI)":[64],"can":[66,189],"explicitly":[67],"leverage":[68],"advantages":[70,102],"both":[72],"segmentation-based":[74,105],"regression-based":[78,87],"methods.":[79],"Specifically,":[80],"exploit":[82],"an":[83],"extra":[84,139],"branch":[85],"with":[86],"auxiliary":[91,127],"module":[92,128],"after":[93],"main":[95,164],"module.":[96],"It":[97],"not":[98],"only":[99,129],"maintains":[100],"detail":[109],"segmentation,":[110],"but":[111],"also":[112],"enables":[113],"model":[115],"sufficient":[119],"understanding":[120],"shape.":[124],"Besides,":[125],"takes":[130],"part":[131],"training,":[134],"there":[136],"is":[137,167],"no":[138],"cost":[140],"prediction.":[143],"further":[145],"improve":[146],"quality":[148],"detection,":[151],"introduce":[153],"novel":[155],"direction-sensitive":[156],"block":[157],"(DSB)":[158],"ERFNet":[161],"module,":[165],"more":[168],"sensitive":[169],"direction":[172],"information":[173],"image,":[176],"so":[177],"obtain":[180],"better":[181],"performance.":[182],"Extensive":[183],"experiments":[184],"CULane":[187],"dataset":[188],"demonstrate":[190],"that":[191],"our":[192],"method":[193],"outperforms":[194],"other":[195],"achieves":[198],"state-of-the-art.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}