太原理工大学学报2024，Vol.55Issue(4)：696-704,9.DOI:10.16355/j.tyut.1007-9432.20230705
基于改进YOLOv8的轻量化钢筋端面检测算法研究
Lightweight Rebar End Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
[Purposes]Rebar plays an indispensable role in construction engineering;however,challenges such as densely packed end faces,non-uniform diameter scales,adhesive boundaries,background fusion,and occlusions between end faces have made precise counting a significant challenge.In recent years,deep learning has made remarkable strides in the field of dense object counting.Nonetheless,deep leaming faces limitations because of the need for large-scale data and computational resources,hindering its practical application.[Methods]In response to these chal-lenges,an enhanced YOLOv8 model framework is introduced for rebar end detection.The framework incorporates Spatial and Channel reconstruction Convolutional(SCConv)modules and the Normalized Wasserstein Distance(NWD)loss function tailored for small object detection.[Findings]Experimental results from ablation tests demonstrate that the SCConv module signifi-cantly reduces network parameters while maintains network performance.Furthermore,the NWD loss function notably enhances the accuracy of rebar end detection in large models.This re-search provides an effective solution for achieving high-precision and lightweight rebar counting.关键词
深度学习/YOLOv8/钢筋计数/检测方法Key words
deep learning/YOLOv8/rebar counting/detection method分类
建筑与水利引用本文复制引用
倪富陶,李倩,聂云靖,王永宝,陈玉发..基于改进YOLOv8的轻量化钢筋端面检测算法研究[J].太原理工大学学报,2024,55(4):696-704,9.基金项目
国家自然科学基金资助项目(52308325) （52308325）
山西省基础研究计划青年项目(20210302124651,20210302124674) （20210302124651,20210302124674）
贵州省科技厅科研项目(黔科合支撑[2021]) （黔科合支撑[2021]）
