{"id":"https://openalex.org/W4402916750","doi":"https://doi.org/10.1109/icip51287.2024.10648025","title":"Crocos-V1: Enhancing Mask Leakage and Bounding Box Localization for Real-Time Crop/Weed Instance Segmentation*","display_name":"Crocos-V1: Enhancing Mask Leakage and Bounding Box Localization for Real-Time Crop/Weed Instance Segmentation*","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402916750","doi":"https://doi.org/10.1109/icip51287.2024.10648025"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10648025","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10648025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","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/A5067696038","display_name":"J. Franco-Robles","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I65806277","display_name":"Universit\u00e9 de Limoges","ror":"https://ror.org/02cp04407","country_code":"FR","type":"education","lineage":["https://openalex.org/I65806277"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jesus Franco-Robles","raw_affiliation_strings":["XLIM Research Institute, UMR CNRS 7252, University of Limoges,Limoges,France,87000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"XLIM Research Institute, UMR CNRS 7252, University of Limoges,Limoges,France,87000","institution_ids":["https://openalex.org/I65806277","https://openalex.org/I1294671590"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041285370","display_name":"Jorge E. Avil\u00e9s-Mejia","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I65806277","display_name":"Universit\u00e9 de Limoges","ror":"https://ror.org/02cp04407","country_code":"FR","type":"education","lineage":["https://openalex.org/I65806277"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jorge E. Avil\u00e9s-Mejia","raw_affiliation_strings":["XLIM Research Institute, UMR CNRS 7252, University of Limoges,Limoges,France,87000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"XLIM Research Institute, UMR CNRS 7252, University of Limoges,Limoges,France,87000","institution_ids":["https://openalex.org/I65806277","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109054404","display_name":"Ouiddad Labbani-Igbida","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I65806277","display_name":"Universit\u00e9 de Limoges","ror":"https://ror.org/02cp04407","country_code":"FR","type":"education","lineage":["https://openalex.org/I65806277"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ouiddad Labbani-Igbida","raw_affiliation_strings":["XLIM Research Institute, UMR CNRS 7252, University of Limoges,Limoges,France,87000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"XLIM Research Institute, UMR CNRS 7252, University of Limoges,Limoges,France,87000","institution_ids":["https://openalex.org/I65806277","https://openalex.org/I1294671590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.08598597,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs/1706.06782","issue":null,"first_page":"444","last_page":"450"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9789999723434448,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9491000175476074,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6694380640983582},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6141138076782227},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.6031235456466675},{"id":"https://openalex.org/keywords/leakage","display_name":"Leakage (economics)","score":0.5983458757400513},{"id":"https://openalex.org/keywords/weed","display_name":"Weed","score":0.546007513999939},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5457912087440491},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5413886904716492},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4398413300514221},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.320750892162323},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.09350687265396118},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.084259033203125}],"concepts":[{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6694380640983582},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6141138076782227},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.6031235456466675},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.5983458757400513},{"id":"https://openalex.org/C2775891814","wikidata":"https://www.wikidata.org/wiki/Q101879","display_name":"Weed","level":2,"score":0.546007513999939},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5457912087440491},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5413886904716492},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4398413300514221},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.320750892162323},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.09350687265396118},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.084259033203125},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","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/icip51287.2024.10648025","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10648025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.7900000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2120323262","https://openalex.org/W2128866545","https://openalex.org/W2524954406","https://openalex.org/W2586545389","https://openalex.org/W2601564443","https://openalex.org/W2621203588","https://openalex.org/W2737250466","https://openalex.org/W2963150697","https://openalex.org/W2980464326","https://openalex.org/W2993182889","https://openalex.org/W3023939003","https://openalex.org/W3035049382","https://openalex.org/W3047032303","https://openalex.org/W3047050685","https://openalex.org/W3085719762","https://openalex.org/W3093600664","https://openalex.org/W3197875528","https://openalex.org/W4293391988","https://openalex.org/W4312460151","https://openalex.org/W4312972582","https://openalex.org/W6733074500","https://openalex.org/W6739941050","https://openalex.org/W6781358803","https://openalex.org/W6784930956"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"This":[0],"paper":[1],"exposes":[2],"a":[3,17,24],"new":[4],"algorithm":[5,39],"designed":[6],"to":[7,127],"enhance":[8],"the":[9,32,42,88,103],"real-time":[10,141],"crop/weed":[11],"instance":[12,21,70,118],"segmentation.":[13,68],"The":[14,37,49,94,108],"approach":[15],"combines":[16],"learning-based":[18],"method":[19,46],"for":[20,41,130,136],"segmentation":[22,56,71,115,119,123],"with":[23,116],"feature":[25,114],"model-based":[26],"image":[27,50],"processing":[28,51],"strategy":[29,52],"that":[30,111],"leverages":[31],"vegetation":[33],"characteristics":[34],"of":[35,44,90],"crops.":[36],"proposed":[38],"compensates":[40],"shortcomings":[43],"each":[45],"performing":[47],"independently.":[48],"achieves":[53],"precise":[54],"crop":[55],"by":[57],"generating":[58],"finely":[59],"refined":[60],"masks;":[61],"but":[62,82],"may":[63],"introduce":[64],"errors":[65],"in":[66,75,87,98],"weed":[67],"Conversely,":[69],"methods":[72,120],"perform":[73],"well":[74],"accurately":[76],"identifying":[77],"both":[78],"crops":[79,132],"and":[80,133],"weeds,":[81],"can":[83],"produce":[84],"imperfect":[85],"masks":[86],"presence":[89],"inaccurate":[91],"bounding":[92],"boxes.":[93],"experiments":[95],"are":[96],"conducted":[97],"different":[99],"evaluation":[100],"campaigns":[101],"including":[102],"ACRE":[104],"international":[105],"competition":[106],"framework.":[107],"results":[109],"demonstrate":[110],"integrating":[112],"color":[113],"state-of-the-art":[117],"improves":[121],"overall":[122],"accuracy,":[124],"achieving":[125],"up":[126],"0.80":[128],"mAP":[129,135],"maize":[131],"0.83":[134],"bean":[137],"crops,":[138],"while":[139],"maintaining":[140],"computational":[142],"efficiency.":[143]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
