{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:32:48Z","timestamp":1779294768295,"version":"3.51.4"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3203198","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T19:38:03Z","timestamp":1661974683000},"page":"97758-97766","source":"Crossref","is-referenced-by-count":17,"title":["An Enhanced YOLOv4 Model With Self-Dependent Attentive Fusion and Component Randomized Mosaic Augmentation for Metal Surface Defect Detection"],"prefix":"10.1109","volume":"10","author":[{"given":"Chenglong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziran","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101619"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3788\/aos201838.0815002"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCTET.2014.6966360"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-016-9937-x"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/IAEAC.2018.8577540"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2012.2184959"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.11.030"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICMSC.2017.7959451"},{"issue":"2","key":"ref9","first-page":"97","article-title":"On-stream defect detection of metal artifacts from line CCD image","volume":"3","author":"Kurokawa","year":"2009","journal-title":"Int. J. Intell. Comput. Med. Sci. Image Process."},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2014.10.009"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12334"},{"key":"ref12","article-title":"Defect detection and classification of galvanized stamping parts based on fully convolution neural network","volume-title":"Proc. SPIE","volume":"10615","author":"Xiao"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2894863"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/info13030124"},{"key":"ref15","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture11090863"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3176956"},{"key":"ref18","article-title":"YOLOv4: Optimal speed and accuracy of object detection","volume-title":"arXiv:2004.10934","author":"Bochkovskiy","year":"2020"},{"key":"ref19","article-title":"YOLOX: Exceeding YOLO series in 2021","volume-title":"arXiv:2107.08430","author":"Ge","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.l007\/978-3-319-46448-0_2"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref24","article-title":"YOLOv3: An incremental improvement","volume-title":"arXiv:1804.02767","author":"Redmon","year":"2018"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01161"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref32","first-page":"1","article-title":"R-FCN: Object detection via region-based fully convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Dai"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-013-5397-8"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/SICE.2006.314681"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/app8091575"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2022.04.028"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09870791.pdf?arnumber=9870791","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T12:28:47Z","timestamp":1706790527000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9870791\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3203198","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}