{"id":"https://openalex.org/W4405785228","doi":"https://doi.org/10.1109/iros58592.2024.10801572","title":"LDIP: Real-time on-road object detection with depth estimation from a single image","display_name":"LDIP: Real-time on-road object detection with depth estimation from a single image","publication_year":2024,"publication_date":"2024-10-14","ids":{"openalex":"https://openalex.org/W4405785228","doi":"https://doi.org/10.1109/iros58592.2024.10801572"},"language":"en","primary_location":{"id":"doi:10.1109/iros58592.2024.10801572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10801572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5101103142","display_name":"Chengpeng Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengpeng Xu","raw_affiliation_strings":["University of Science and Technology of China,Institute of Advanced Technology,Hefei,China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China,Institute of Advanced Technology,Hefei,China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111783512","display_name":"Xiao Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Sun","raw_affiliation_strings":["Hefei University of Technology,School of Computer Science and Technology,Hefei,China"],"affiliations":[{"raw_affiliation_string":"Hefei University of Technology,School of Computer Science and Technology,Hefei,China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018696423","display_name":"Yangyang Xu","orcid":"https://orcid.org/0009-0002-5406-5961"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangyang Xu","raw_affiliation_strings":["University of Science and Technology of China,Institute of Advanced Technology,Hefei,China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China,Institute of Advanced Technology,Hefei,China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100746364","display_name":"Ruolin Wang","orcid":"https://orcid.org/0000-0003-4801-4105"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruolin Wang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101103142"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.2527,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57419355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"9455","last_page":"9460"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9975000023841858,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9975000023841858,"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":0.9905999898910522,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9898999929428101,"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":0.7007756233215332},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6154422760009766},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5487363338470459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5201157331466675},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49000731110572815},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48421263694763184},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.46801862120628357},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.24473881721496582},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12320601940155029}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7007756233215332},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6154422760009766},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5487363338470459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5201157331466675},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49000731110572815},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48421263694763184},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.46801862120628357},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.24473881721496582},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12320601940155029},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros58592.2024.10801572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10801572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":25,"referenced_works":["https://openalex.org/W1230023165","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W1968227650","https://openalex.org/W2110917409","https://openalex.org/W2125310925","https://openalex.org/W2144528190","https://openalex.org/W2193145675","https://openalex.org/W2949208911","https://openalex.org/W2963037989","https://openalex.org/W2963087201","https://openalex.org/W2963488291","https://openalex.org/W2986782335","https://openalex.org/W3034653753","https://openalex.org/W3108179104","https://openalex.org/W3118453581","https://openalex.org/W4210733301","https://openalex.org/W4214520160","https://openalex.org/W4312939270","https://openalex.org/W4385245566","https://openalex.org/W4386076547","https://openalex.org/W4401414004","https://openalex.org/W6620707391","https://openalex.org/W6685261749","https://openalex.org/W6784333009"],"related_works":["https://openalex.org/W2576994247","https://openalex.org/W2608353378","https://openalex.org/W4249206767","https://openalex.org/W2563559453","https://openalex.org/W2382330008","https://openalex.org/W1519970947","https://openalex.org/W2737719445","https://openalex.org/W4243860260","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Detecting":[0],"on-road":[1],"objects":[2,29,96],"with":[3],"absolute":[4,91],"depth":[5,45,51,92,136],"information":[6,52,93],"is":[7,191,220],"one":[8],"of":[9,129,133,140,175,181,201],"the":[10,78,98,107,151,157,171],"most":[11],"crucial":[12],"tasks":[13,47,125],"in":[14,30,60,97,122,135,145,184,215],"autonomous":[15,109],"driving":[16,110],"to":[17,25,58,64,86,186],"ensure":[18],"safety.":[19],"Traditional":[20],"2D":[21],"object":[22,41,123],"detection":[23,42,46],"aims":[24],"classify":[26],"and":[27,43,80,90,104,126,131,161,166,173],"locate":[28],"image":[31],"space,":[32],"but":[33],"it":[34,213],"cannot":[35],"acquire":[36],"in-depth":[37],"information.":[38],"While":[39],"3D":[40],"pixel-level":[44],"can":[48],"provide":[49,87],"accurate":[50],"for":[53,94],"objects,":[54],"they":[55],"are":[56],"challenging":[57],"deploy":[59],"real-world":[61],"scenarios":[62],"due":[63],"their":[65],"significant":[66],"inference":[67,199],"overhead.":[68],"This":[69,169],"paper":[70],"proposes":[71],"a":[72,119],"novel":[73],"deep":[74],"learning-based":[75],"model":[76,102,153,177,190],"named":[77],"Location":[79],"Depth":[81],"Information":[82],"Perceptron":[83],"(LDIP),":[84],"designed":[85],"positional,":[88],"categorical,":[89],"given":[95],"images.We":[99],"first":[100],"conducted":[101],"training":[103],"validation":[105],"on":[106,154,206],"vehicle-side":[108],"dataset\u2014KITTI.":[111],"The":[112],"experimental":[113],"results":[114],"show":[115],"that":[116],"we":[117,149],"achieved":[118],"68.6%":[120],"mAP":[121],"recognition":[124],"an":[127,198,207],"RMSE":[128,162],"0.101":[130],"AbsRel":[132],"2.327":[134],"estimation":[137],"tasks,":[138],"all":[139],"which":[141],"represent":[142],"state-of-the-art":[143],"performance":[144],"comparable":[146],"tasks.":[147],"Subsequently,":[148],"fine-tuned":[150],"trained":[152],"DAIR,":[155],"where":[156],"validated":[158],"mAP,":[159],"AbsRel,":[160],"reached":[163],"65.4%,":[164],"0.092,":[165],"2.461":[167],"respectively.":[168],"demonstrates":[170],"robustness":[172],"generalization":[174],"our":[176,189],"across":[178],"different":[179],"types":[180],"road":[182],"datasets.Moreover,":[183],"comparison":[185],"other":[187],"models,":[188],"more":[192],"compact":[193],"while":[194],"maintaining":[195],"accuracy,":[196],"achieving":[197],"speed":[200],"70":[202],"frames":[203],"per":[204],"second":[205],"NVIDIA":[208],"4060":[209],"GPU,":[210],"thus":[211],"making":[212],"deployable":[214],"practical":[216],"scenarios.":[217],"Relevant":[218],"code":[219],"available":[221],"at":[222],"https://github.com/xcp-ustc/LDIP.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-10-10T00:00:00"}