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Soc."},{"key":"ref30","article-title":"Application research of improved YOLO V3 algorithm in PCB electronic component detection","volume":"99","author":"Li","year":"2019, Art. no. 3750","journal-title":"Appl. Sci."},{"key":"ref31","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extract."},{"key":"ref32","series-title":"Proc. IEEE Int. Conf. Pow. Electron., Comput. Appl. (ICPECA)","first-page":"1009","article-title":"An improved YOLOv3 method for PCB surface defect detection","author":"Lan","year":"2021"},{"key":"ref33","unstructured":"A. Bochkovskiy, C. -Y. Wang, and H. -Y. M. 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