{"id":"https://openalex.org/W4212826194","doi":"https://doi.org/10.1109/jiot.2022.3151374","title":"Lightweight Monocular Depth Estimation on Edge Devices","display_name":"Lightweight Monocular Depth Estimation on Edge Devices","publication_year":2022,"publication_date":"2022-02-15","ids":{"openalex":"https://openalex.org/W4212826194","doi":"https://doi.org/10.1109/jiot.2022.3151374"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2022.3151374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2022.3151374","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"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 Internet of Things Journal","raw_type":"journal-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/A5000537525","display_name":"Siping Liu","orcid":"https://orcid.org/0000-0003-0019-5154"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siping Liu","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-0019-5154","affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049154222","display_name":"Laurence T. Yang","orcid":"https://orcid.org/0000-0002-7986-4244"},"institutions":[{"id":"https://openalex.org/I197191942","display_name":"St. Francis Xavier University","ror":"https://ror.org/01wcaxs37","country_code":"CA","type":"education","lineage":["https://openalex.org/I197191942"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Laurence Tianruo Yang","raw_affiliation_strings":["Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada"],"raw_orcid":"https://orcid.org/0000-0002-7986-4244","affiliations":[{"raw_affiliation_string":"Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada","institution_ids":["https://openalex.org/I197191942"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036091000","display_name":"Xiaohan Tu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210152160","display_name":"Zhengzhou Railway Vocational & Technical College","ror":"https://ror.org/04zs83x19","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohan Tu","raw_affiliation_strings":["Department of Image and Network Investigation, Railway Police College, Zhengzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4330-240X","affiliations":[{"raw_affiliation_string":"Department of Image and Network Investigation, Railway Police College, Zhengzhou, China","institution_ids":["https://openalex.org/I4210152160"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019444969","display_name":"Renfa Li","orcid":"https://orcid.org/0000-0003-4573-7375"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renfa Li","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-4573-7375","affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100765171","display_name":"Cheng Xu","orcid":"https://orcid.org/0000-0002-1323-3175"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Xu","raw_affiliation_strings":["College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-1323-3175","affiliations":[{"raw_affiliation_string":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5991,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.91147599,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"9","issue":"17","first_page":"16168","last_page":"16180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9983000159263611,"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":0.8492871522903442},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6123952269554138},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5284450054168701},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.523561418056488},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5137654542922974},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.49886322021484375},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4889043867588043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47817450761795044},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3922022581100464},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3858814835548401},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32096970081329346},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13553950190544128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8492871522903442},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6123952269554138},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5284450054168701},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.523561418056488},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5137654542922974},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.49886322021484375},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4889043867588043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47817450761795044},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3922022581100464},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3858814835548401},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32096970081329346},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13553950190544128},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2022.3151374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2022.3151374","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G576546066","display_name":null,"funder_award_id":"61932010","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7556816975","display_name":"CPS\u4f8b\u5316\u2014\u2014\u9ad8\u94c1\u63a5\u89e6\u7f51\u667a\u80fd\u5de1\u68c0\u673a\u5668\u4eba\u7814\u7a76","funder_award_id":"61772185","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1905829557","https://openalex.org/W2132947399","https://openalex.org/W2194775991","https://openalex.org/W2798373498","https://openalex.org/W2960206397","https://openalex.org/W2961343177","https://openalex.org/W2962960377","https://openalex.org/W2963045776","https://openalex.org/W2963122961","https://openalex.org/W2963163009","https://openalex.org/W2963359474","https://openalex.org/W2963446712","https://openalex.org/W2963583471","https://openalex.org/W2963591054","https://openalex.org/W2967115342","https://openalex.org/W2987175876","https://openalex.org/W2988910664","https://openalex.org/W3009257710","https://openalex.org/W3014263713","https://openalex.org/W3024745646","https://openalex.org/W3034421932","https://openalex.org/W3034723120","https://openalex.org/W3035414587","https://openalex.org/W3082740509","https://openalex.org/W3107156787","https://openalex.org/W3127857540","https://openalex.org/W3133399029","https://openalex.org/W3173727695","https://openalex.org/W3194390908","https://openalex.org/W3201971426","https://openalex.org/W3203439539","https://openalex.org/W6684191040","https://openalex.org/W6684921986","https://openalex.org/W6685261749","https://openalex.org/W6748839928","https://openalex.org/W6766261854","https://openalex.org/W6766978945","https://openalex.org/W6785377559"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2095407248","https://openalex.org/W1997714924","https://openalex.org/W2129190845","https://openalex.org/W4389045637","https://openalex.org/W1994209155","https://openalex.org/W2076843925","https://openalex.org/W3092613710","https://openalex.org/W4384300015","https://openalex.org/W2054979592"],"abstract_inverted_index":{"Given":[0],"monocular":[1,5],"images":[2],"as":[3,42],"inputs,":[4],"depth":[6,84,95,113],"estimation":[7],"(MDE)":[8],"infers":[9],"pixel-level":[10],"depth.":[11],"MDE":[12,25,77,167],"is":[13,183,246],"always":[14],"a":[15,118],"critical":[16],"stage":[17],"in":[18,48,57,85,203],"scene":[19],"sensing":[20],"on":[21,78,131,174,194,229],"edge":[22,80,196],"devices.":[23,81],"Existing":[24],"studies":[26],"frequently":[27],"employ":[28],"deep":[29],"neural":[30],"networks":[31],"(DNNs)":[32],"for":[33,50,59,73],"MDE,":[34],"but":[35],"they":[36],"still":[37],"face":[38],"some":[39],"problems,":[40],"such":[41],"sacrificing":[43],"computational":[44,99],"complexity":[45,142],"and":[46,75,128,140,149,227,231],"efficiency":[47],"return":[49],"great":[51],"precision,":[52],"or":[53,205],"losing":[54,146],"more":[55],"precision":[56],"exchange":[58],"increased":[60],"efficiency.":[61],"To":[62],"alleviate":[63],"these":[64],"issues;":[65],"1)":[66],"we":[67,88,116,151],"propose":[68],"an":[69,170],"encoder\u2013decoder":[70],"network":[71],"(EdgeNet)":[72],"precise":[74],"fast":[76],"different":[79,110,195],"When":[82],"recovering":[83],"the":[86,102,105,126,153,175,180,212,215,217,222,243],"decoder,":[87],"design":[89],"upsampling":[90],"modules":[91],"to":[92,156],"aggregate":[93],"global":[94],"information":[96],"with":[97,211,235],"low":[98],"complexity,":[100],"improving":[101],"accuracy":[103],"of":[104,112,143,172,214],"decoder":[106,129],"by":[107,169,225],"extracting":[108],"its":[109],"ranges":[111],"information;":[114],"2)":[115],"develop":[117],"two-stage":[119],"channel":[120],"pruning":[121,135],"method":[122,136],"to,":[123],"respectively,":[124,220],"prune":[125],"encoder":[127],"based":[130],"their":[132],"characteristics.":[133],"Our":[134],"further":[137],"reduces":[138,221],"latency":[139,224],"model/computational":[141],"EdgeNet,":[144,219],"while":[145],"little":[147],"accuracy;":[148],"3)":[150],"optimize":[152],"pruned":[154],"EdgeNet":[155],"decrease":[157],"graphics":[158],"processing":[159],"unit":[160],"(GPU)":[161],"scheduling":[162],"overhead.":[163],"The":[164],"optimization":[165],"accelerates":[166],"inference":[168],"order":[171],"magnitude":[173],"TX2":[176,232],"GPU":[177,197,223,233],"device,":[178],"when":[179,199,242],"input":[181,200,244],"resolution":[182,245],"224$\\times":[184],"$224.":[185],"Extensive":[186],"experiments":[187],"show":[188],"that":[189],"our":[190],"strategies":[191],"are":[192],"effective":[193],"devices,":[198],"resolutions":[201],"differ":[202],"outdoor":[204],"indoor":[206],"scenes.":[207],"For":[208],"example,":[209],"compared":[210],"state":[213],"art,":[216],"optimized":[218],"76.3%":[226],"89.2%":[228],"Nano":[230],"devices":[234],"2.6%":[236],"lower":[237],"root":[238],"mean":[239],"square":[240],"error":[241],"128$\\times":[247],"$416.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":6}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}
