Specifically, to improve the model's performance speed, we simplify CSPDarknet53 and path aggregation network (PANet) for the feature extraction stage of YOLOv4 ...
A newer version of YOLOv4 was further enhanced and proposed for lumber surface defect detection, focusing on improving detection performance while ...
A deep learning-based surface defect detection system with a proposed “lightning YOLOv4” model that improves the average precision of defect localization ...
A surface defects detection method based on improved YOLOv3 for sawn lumbers is proposed, which replaces the intersection over union (IoU) loss for ...
Lightning YOLOv4 for a surface defect detection system for sawn lumber. F Akhyar, L Novamizanti, T Putra, EN Furqon, MC Chang, CY Lin. 2022 IEEE 5th ...
Lightning YOLOv4 for a Surface Defect Detection System for Sawn Lumber. MIPR 2022: 184-189. [+][–]. Coauthor network. maximize. Note that this feature is a work ...
This study proposes a defect detection system on the surface of pine wood and ... Lightning YOLOv4 for a Surface Defect Detection System for Sawn Lumber.
identification system of wood surface defects based on current state-of-the-art detection methods such as. YOLOv5[22] with several modifications for better ...
Missing: Lightning Lumber.
Lightning YOLOv4 for a Surface Defect Detection System for Sawn Lumber. 2022 IEEE 5th International Conference on Multimedia Information Processing and ...
This paper introduces an efficient and precise approach to detecting wood surface defects, building upon enhancements to the YOLOv8 model.