现代信息科技2025，Vol.9Issue(7)：58-63,70,7.DOI:10.19850/j.cnki.2096-4706.2025.07.011
基于改进YOLOv8n的轻量化工地堆放木材异常检测算法
A Lightweight Algorithm for Stacked Timber Anomaly Detection on Construction Sites Based on Improved YOLOv8n
王浩宇1
作者信息
- 1. 太原师范学院计算机科学与技术学院,山西 晋中 030619
- 折叠
摘要
Abstract
When the timber materials are stacked on the construction site,the outdoor environment is prone to abnormal problems such as moisture deformation and dry cracking on the surface of the timber.Aiming at the problems of poor accuracy and high computational complexity of the existing detection algorithms on the surface of timber materials,a lightweight small target detection algorithm(YOLO-ESN)based on YOLOv8n is proposed.The algorithm introduces the Spatial and Channel Reconstruction Convolution(SCConv)module and the Normalized Wasserstein Distance(NWD)loss function for small target detection.At the same time,it embeds the Efficient Multi-Scale Attention(EMA)module based on Cross-Spatial Learning into the backbone network to reduce the impact of occlusion and background interference.The improved algorithm is experimentally verified on the timber defect dataset.Compared with the original algorithm,its mAP@0.5 is increased by 3.6%,and the parameter quantity is reduced by 23.3%,which realizes the real-time and accurate detection of the abnormal situation of stacked timber materials.关键词
改进YOLOv8n算法/工地木材异常检测/轻量化/小目标检测Key words
improved YOLOv8n algorithm/construction site timber anomaly detection/lightweight/small target detection分类
信息技术与安全科学引用本文复制引用
王浩宇..基于改进YOLOv8n的轻量化工地堆放木材异常检测算法[J].现代信息科技,2025,9(7):58-63,70,7.
