{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T05:56:45Z","timestamp":1768888605530,"version":"3.49.0"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation (IITP)-Information Technology Research Center (ITRC) grant"},{"name":"Korean Government [Ministry of Science and Information and Communication Technology (ICT)]","award":["RS-2021-II212052"],"award-info":[{"award-number":["RS-2021-II212052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3603536","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T18:32:22Z","timestamp":1756319542000},"page":"151189-151201","source":"Crossref","is-referenced-by-count":1,"title":["LLTQ+: A Hardware-Friendly Quantization Framework for Modern YOLO Architectures"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1821-8902","authenticated-orcid":false,"given":"Yugwon","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8448-9680","authenticated-orcid":false,"given":"Jaemyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3752-3740","authenticated-orcid":false,"given":"Jin-Ku","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1011-2319","authenticated-orcid":false,"given":"Yongwoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Technology Education, Korea National University of Education, Cheongju, Republic of Korea"}]}],"member":"263","reference":[{"issue":"3","key":"ref1","doi-asserted-by":"crossref","first-page":"5540","DOI":"10.52783\/jes.6453","article-title":"Deep learning in computer vision: A critical review","volume":"20","author":"Kumar","year":"2024","journal-title":"J. Electr. Syst."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1186\/s13636-024-00329-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref5","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.10934"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2209.02976"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref10","first-page":"107984","article-title":"YOLOv10: Real-time end-to-end object detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref11","article-title":"YOLOv11: An overview of the key architectural enhancements","author":"Khanam","year":"2024","journal-title":"arXiv:2410.17725"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref14","article-title":"Improving post training neural quantization: Layer-wise calibration and integer programming","author":"Hubara","year":"2020","journal-title":"arXiv:2006.10518"},{"key":"ref15","first-page":"7948","article-title":"Post training 4-bit quantization of convolutional networks for rapid-deployment","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Banner"},{"key":"ref16","article-title":"BRECQ: Pushing the limit of post-training quantization by block reconstruction","author":"Li","year":"2021","journal-title":"arXiv:2102.05426"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01161"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00156"},{"key":"ref19","article-title":"PACT: Parameterized clipping activation for quantized neural networks","author":"Choi","year":"2018","journal-title":"arXiv:1805.06085"},{"key":"ref20","article-title":"Learned step size quantization","author":"Esser","year":"2019","journal-title":"arXiv:1902.08153"},{"key":"ref21","first-page":"112","article-title":"Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Jain"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3099075"},{"key":"ref23","article-title":"MQBench: Towards reproducible and deployable model quantization benchmark","author":"Li","year":"2021","journal-title":"arXiv:2111.03759"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.29045"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-025-06418-0"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3232258"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/app122312405"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/app15073980"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/electronics14030504"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47665-5_25"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-025-04234-0"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref34","article-title":"Deep learning using rectified linear units (ReLU)","author":"Agarap","year":"2018","journal-title":"arXiv:1803.08375"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11142781.pdf?arnumber=11142781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T18:22:56Z","timestamp":1757010176000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11142781\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3603536","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}