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

作　　者：王浩宇 WANG Haoyu(College of Computer Science and Technology,Taiyuan Normal University,Jinzhong 030619,China)

机构地区：[1]太原师范学院计算机科学与技术学院,山西晋中030619

出　　处：《现代信息科技》2025年第7期58-63,70,共7页Modern Information Technology

摘　　要：在工地堆放木材物料时,室外环境容易导致木材表面出现受潮变形、干裂等异常状况。针对现有检测算法在木材物料表面异常检测方面精度欠佳、模型计算复杂度高等问题,提出一种以YOLOv8n为基础模型的轻量级小目标检测算法(YOLO-ESN)。该算法引入空间-通道重构卷积(SCConv)模块以及针对小目标检测的归一化Wasserstein距离(NWD)损失函数,同时将基于跨空间学习的高效多尺度注意力模块(EMA)嵌入主干网络,以此减轻遮挡及背景干扰带来的影响。改进后的算法在木材缺陷数据集上进行了实验验证,相较于原算法,其mAP@0.5提升了3.6%,参数量降低了23.3%,实现了对堆放木材物料异常情况的实时准确检测。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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391[自动化与计算机技术—控制科学与工程]