{"id":"https://openalex.org/W3033391883","doi":"https://doi.org/10.1109/vts48691.2020.9107565","title":"On Classification of Acceptable Images for Reliable Artificial Intelligence Systems: A Case Study on Pedestrian Detection","display_name":"On Classification of Acceptable Images for Reliable Artificial Intelligence Systems: A Case Study on Pedestrian Detection","publication_year":2020,"publication_date":"2020-04-01","ids":{"openalex":"https://openalex.org/W3033391883","doi":"https://doi.org/10.1109/vts48691.2020.9107565","mag":"3033391883"},"language":"en","primary_location":{"id":"doi:10.1109/vts48691.2020.9107565","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vts48691.2020.9107565","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 38th VLSI Test Symposium (VTS)","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/A5028476747","display_name":"Tong-Yu Hsieh","orcid":"https://orcid.org/0000-0002-7954-5569"},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Tong-Yu Hsieh","raw_affiliation_strings":["Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035572123","display_name":"Pin-Xuan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Pin-Xuan Wu","raw_affiliation_strings":["Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030728145","display_name":"Chun-Chao Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I142974352","display_name":"National Sun Yat-sen University","ror":"https://ror.org/00mjawt10","country_code":"TW","type":"education","lineage":["https://openalex.org/I142974352"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chun-Chao Cheng","raw_affiliation_strings":["Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan","institution_ids":["https://openalex.org/I142974352"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5028476747"],"corresponding_institution_ids":["https://openalex.org/I142974352"],"apc_list":null,"apc_paid":null,"fwci":0.4885,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65092088,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.7627868056297302},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6639018058776855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6307968497276306},{"id":"https://openalex.org/keywords/error-detection-and-correction","display_name":"Error detection and correction","score":0.5768870115280151},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5760247707366943},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4376697540283203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42886537313461304},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41047656536102295},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40531080961227417},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33096957206726074},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3190731406211853},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.23056307435035706},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10814324021339417},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09630846977233887}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7627868056297302},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6639018058776855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6307968497276306},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.5768870115280151},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5760247707366943},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4376697540283203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42886537313461304},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41047656536102295},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40531080961227417},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33096957206726074},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3190731406211853},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.23056307435035706},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10814324021339417},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09630846977233887},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vts48691.2020.9107565","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vts48691.2020.9107565","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 38th VLSI Test Symposium (VTS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1441545499","https://openalex.org/W1971212236","https://openalex.org/W2024046085","https://openalex.org/W2040903332","https://openalex.org/W2099971661","https://openalex.org/W2125556102","https://openalex.org/W2133665775","https://openalex.org/W2141983208","https://openalex.org/W2161969291","https://openalex.org/W2613347744","https://openalex.org/W2615622084","https://openalex.org/W2785462806","https://openalex.org/W2890593271","https://openalex.org/W4240589735"],"related_works":["https://openalex.org/W2093384100","https://openalex.org/W2624903463","https://openalex.org/W3207051105","https://openalex.org/W4386960967","https://openalex.org/W2083896825","https://openalex.org/W4313452767","https://openalex.org/W2802018156","https://openalex.org/W2994634013","https://openalex.org/W2980971426","https://openalex.org/W4290774787"],"abstract_inverted_index":{"Images":[0],"are":[1,89,125],"essential":[2],"data":[3,26],"for":[4,48],"many":[5],"artificial":[6],"intelligence":[7],"(AI)":[8],"systems":[9,50],"such":[10],"as":[11,149],"pedestrian":[12,181,209],"detection.":[13],"However,":[14],"image":[15,19,25,45],"processing":[16],"circuits":[17],"or":[18,30],"storage":[20],"devices":[21],"may":[22],"produce":[23],"erroneous":[24,186],"due":[27],"to":[28,78,81,120,162,180,213],"aging":[29],"radiation.":[31],"In":[32,96,139],"this":[33,97],"paper":[34],"we":[35,51,99],"will":[36],"show":[37,172,195],"that":[38,53,173,196],"there":[39],"actually":[40],"exists":[41],"much":[42],"tolerability":[43,55,105],"in":[44,129],"errors.":[46],"Moreover,":[47],"AI":[49,83],"find":[52],"the":[54,82,150,176,185,208],"is":[56],"even":[57],"larger.":[58],"This":[59,73,153],"finding":[60],"provides":[61],"an":[62],"attractive":[63],"reliability":[64,166],"enhancement":[65,167],"solution":[66,74],"by":[67],"classifying":[68],"and":[69,102,110,127,136],"filtering":[70],"acceptable":[71,76],"images.":[72],"allows":[75],"images":[77,88,107,148,187],"still":[79],"go":[80],"inference":[84],"process,":[85],"while":[86,216],"unacceptable":[87],"discarded,":[90],"together":[91],"with":[92],"warning":[93],"signals":[94],"activated.":[95],"work,":[98],"first":[100],"evaluate":[101],"compare":[103],"error":[104],"of":[106,116,131,184,207],"from":[108],"human":[109,217],"machine":[111,122],"perspectives.":[112],"Then":[113],"a":[114,157,164],"number":[115],"possible":[117],"test":[118,160,178],"methods":[119,142],"support":[121],"based":[123,219],"error-tolerance":[124,199,220],"discussed":[126],"compared":[128],"terms":[130],"their":[132],"acceptability":[133],"classification":[134],"accuracy":[135],"computation":[137],"cost.":[138],"particular,":[140],"these":[141],"should":[143],"not":[144],"need":[145],"golden":[146],"(error-free)":[147],"comparison":[151],"basis.":[152],"greatly":[154],"facilitates":[155],"developing":[156],"low-cost":[158],"on-line":[159],"architecture":[161],"enable":[163],"real-time":[165],"solution.":[168],"Our":[169],"experimental":[170],"results":[171,193],"when":[174],"applying":[175],"suggested":[177],"method":[179],"detection,":[182],"93.48%":[183],"can":[188,200,221],"be":[189],"correctly":[190],"classified.":[191],"The":[192],"also":[194],"adopting":[197],"machine-based":[198],"extend":[201,222],"MTTF":[202],"(Mean":[203],"Time":[204],"To":[205],"Failure)":[206],"detection":[210],"system":[211],"up":[212],"additional":[214,224],"88.7%,":[215],"vision":[218],"only":[223],"35.1%.":[225]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}