重庆理工大学学报2024，Vol.38Issue(15)：173-180,8.DOI:10.3969/j.issn.1674-8425(z).2024.08.020
改进YOLOv5s的小样本3D打印点阵结构表面缺陷检测
Surface defect detection of small sample for 3D printed lattice structures by improved YOLOv5s model
摘要
Abstract
3D printed lattice structures are widely used in aerospace,machinery,and construction industries,but their surface defect distribution is uneven and weak,often resulting in missed and false detection.To solve the problem,this paper proposes a YOLOv5s-PD model which adds the XSPPF module and the Atrous Spatial Pyramid Pooling module to improve the ability of the model to acquire different defect features.To address the high false detection rate caused by disordered defect distribution on the surface of the 3D printed lattice structure,the ECA module is added to the YOLOv5s model.The SIoU loss function is adopted to consider the inconsistency between the predicted frame and the real frame due to the irregular and large difference in the surface defect size information of the 3D-printed lattice structure.The improved model is employed to detect the surface defect dataset of lattice structures.Our results show that recall of defect detection is 94.0％,and the mAP@0.5 stands at 96.2％.Our proposed improved network achieves automatic identification of surface defects of 3D printed lattice structures.关键词
3D打印/点阵结构/YOLOv5s/缺陷检测/平均精度Key words
3D printing/lattice structure/YOLOv5s/defect detection/mean average precision分类
信息技术与安全科学引用本文复制引用
安治国,鲜青霖,许亮..改进YOLOv5s的小样本3D打印点阵结构表面缺陷检测[J].重庆理工大学学报,2024,38(15):173-180,8.基金项目
重庆市科技局项目(cstc2021jcyj-msxmX1047) （cstc2021jcyj-msxmX1047）
