{"id":"https://openalex.org/W4408401547","doi":"https://doi.org/10.1109/icftic64248.2024.10913230","title":"Illegal Vehicle Intrusion Detection of Oil and Gas Pipelines Based on Improved YOLOv9","display_name":"Illegal Vehicle Intrusion Detection of Oil and Gas Pipelines Based on Improved YOLOv9","publication_year":2024,"publication_date":"2024-12-13","ids":{"openalex":"https://openalex.org/W4408401547","doi":"https://doi.org/10.1109/icftic64248.2024.10913230"},"language":"en","primary_location":{"id":"doi:10.1109/icftic64248.2024.10913230","doi":"https://doi.org/10.1109/icftic64248.2024.10913230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icftic64248.2024.10913230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC)","raw_type":"proceedings-article"},"volume":null,"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/A5039111900","display_name":"Tongyu Wu","orcid":"https://orcid.org/0009-0009-4109-8992","observed_orcids":["https://orcid.org/0009-0009-4109-8992"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tongyu Wu","raw_affiliation_strings":["China National Aviation Fuel Petrochemical Pipeline Co., Ltd,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China National Aviation Fuel Petrochemical Pipeline Co., Ltd,Beijing,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701131","display_name":"Jian Qiao Sun","orcid":"https://orcid.org/0000-0001-9898-3129","observed_orcids":["https://orcid.org/0000-0001-9898-3129","https://orcid.org/0000-0002-9743-7457"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian Sun","raw_affiliation_strings":["China National Aviation Fuel Petrochemical Pipeline Co., Ltd,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China National Aviation Fuel Petrochemical Pipeline Co., Ltd,Beijing,China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Huanjie Wei","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huanjie Wei","raw_affiliation_strings":["China National Aviation Fuel Petrochemical Pipeline Co., Ltd,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China National Aviation Fuel Petrochemical Pipeline Co., Ltd,Beijing,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3414353,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"101","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9984999895095825,"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":0.9984999895095825,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.0003000000142492354,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.0003000000142492354,"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/oil-and-gas-pipelines","display_name":"oil and gas pipelines","score":0.9959999918937683},{"id":"https://openalex.org/keywords/yolov9","display_name":"YOLOv9","score":0.9129999876022339},{"id":"https://openalex.org/keywords/depthwise-separable-convolution","display_name":"depthwise separable convolution","score":0.7879999876022339},{"id":"https://openalex.org/keywords/small-object-detection","display_name":"small object detection","score":0.7990000247955322},{"id":"https://openalex.org/keywords/global-attention-module","display_name":"global attention module","score":0.9350000023841858},{"id":"https://openalex.org/keywords/channel-and-spatial-attention","display_name":"channel and spatial attention","score":0.8119999766349792},{"id":"https://openalex.org/keywords/real-time-object-detection","display_name":"real-time object detection","score":0.8479999899864197}],"concepts":[{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.6398910284042358},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5602601766586304},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.46852558851242065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43309032917022705},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3984343409538269},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3349817395210266},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.214340478181839},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.06530377268791199}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icftic64248.2024.10913230","doi":"https://doi.org/10.1109/icftic64248.2024.10913230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icftic64248.2024.10913230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.7207000255584717}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W4389722379","https://openalex.org/W4395114376","https://openalex.org/W4395453447","https://openalex.org/W6936881376"],"related_works":["https://openalex.org/W2357468538","https://openalex.org/W1577110157","https://openalex.org/W1995889332","https://openalex.org/W3104163240","https://openalex.org/W2355007334","https://openalex.org/W2390009783","https://openalex.org/W4254602698","https://openalex.org/W2394461323","https://openalex.org/W2349441905","https://openalex.org/W3008693296"],"abstract_inverted_index":{"In":[0],"view":[1],"of":[2,5,75,83,98,112,116,127,146,161],"the":[3,12,59,63,73,81,95,99,110,113,117,125,128,152,159,169,184],"problem":[4],"large":[6],"illegal":[7],"vehicles":[8],"being":[9],"constructed":[10],"near":[11],"oil":[13,22],"and":[14,21,23,41,50,62,79,105,135,141,151,163,176,183,191],"gas":[15,24],"pipeline,":[16],"leading":[17],"to":[18,37,46,57,71,94,108],"pipeline":[19],"damage":[20],"leakage,":[25],"an":[26],"improved":[27,129,170],"YOLOv9":[28],"is":[29,35,55,69,92],"proposed.":[30],"First,":[31],"deeply":[32],"separable":[33],"convolution":[34,40],"used":[36,56],"replace":[38,58],"traditional":[39],"extract":[42],"features":[43,115],"in":[44],"stages":[45],"reduce":[47],"network":[48],"complexity":[49],"deployment":[51],"cost.":[52],"Secondly,":[53],"Inner-SloU":[54],"CloU":[60],"loss,":[61],"auxiliary":[64],"frame":[65],"with":[66,138],"zoom":[67],"factor":[68],"introduced":[70],"improve":[72],"accuracy":[74,162],"small":[76],"target":[77],"detection":[78,178],"enhance":[80,109],"effect":[82],"multi-scale":[84],"vehicle":[85],"classification.":[86],"Finally,":[87],"a":[88],"global":[89],"attention":[90,104,107],"module":[91],"added":[93],"front":[96],"end":[97],"network-assisted":[100],"branch,":[101],"combining":[102],"spatial":[103],"channel":[106],"capture":[111],"overall":[114],"vehicle.":[118],"The":[119,144],"objective":[120],"experimental":[121],"results":[122],"showed":[123],"that":[124,168],"mAP":[126],"model":[130,185],"reached":[131],"95.54%,":[132],"2.65%,":[133],"1.82%":[134],"3.33%":[136],"compared":[137],"YOLOvl0m,":[139],"YOLOv8m":[140],"YOLOv5m":[142],"respectively.":[143],"number":[145],"parameters":[147],"was":[148,155],"14.7":[149],"M":[150],"inference":[153],"time":[154],"48ms,":[156],"which":[157],"met":[158],"requirements":[160],"real-time.":[164],"Subjective":[165],"experiments":[166],"show":[167],"method":[171],"has":[172],"lower":[173],"leakage":[174],"rate":[175,179],"false":[177],"than":[180],"other":[181],"models,":[182],"can":[186],"still":[187],"maintain":[188],"good":[189],"robustness":[190],"universality.":[192]},"counts_by_year":[],"updated_date":"2026-09-24T07:57:37.273135","created_date":"2025-10-10T00:00:00"}
