物联网学报2025，Vol.9Issue(3)：132-142,11.DOI:10.11959/j.issn.2096-3750.2025.00464
融合注意力机制的3D打印缺陷检测算法
A 3D printing defect detection algorithm incorporating attention mechanism
摘要
Abstract
In recent years,3D printing technology has played an increasingly important role in a growing number of industries.However,as a relatively new technology,it tends to exhibit more defects during the printing process compared to traditional manufacturing methods.These defects can significantly impact the performance of the final product.Given that 3D printed parts typically have complex and highly optimized geometric shapes,traditional detection technologies struggle to meet the demands for precision and efficiency.To address this challenge,this paper introduces a 3D printing defect detection algorithm based on an improved version of YOLOv5.The algorithm makes extensive refinements to the YOLOv5 model,achieving model lightweighting by replacing the loss function and introducing an attention mechanism.The newly designed detection system is characterized by a smaller parameter scale,rapid inference speed,high detection accuracy,and strong robustness.Compared to the original YOLOv5s model,the improved lightweight model has achieved a detection accuracy of 94.2%,and has nearly halved the parameter scale.This advancement not only enhances detection efficiency but also provides an effective technical solution for 3D printing defect detection and fault diagnosis.关键词
3D打印/缺陷检测/深度学习/YOLOv5/注意力机制Key words
3D printing/defect detection/deep learning/YOLOv5/attention分类
信息技术与安全科学引用本文复制引用
张俊杰,沈震,方启航,董西松,王迪,熊刚..融合注意力机制的3D打印缺陷检测算法[J].物联网学报,2025,9(3):132-142,11.基金项目
国家自然科学基金资助项目(No.92267103,No.92360307,No.62461160259)The National Natural Science Foundation of China(No.92267103,No.92360307,No.62461160259) （No.92267103,No.92360307,No.62461160259）
