航天器环境工程2025，Vol.42Issue(5)：549-556,8.DOI:10.12126/see.2025076
基于YOLOv8网络和AI技术的航天电子设备故障诊断方法研究
A fault diagnosis method for aerospace electronic equipment based on YOLOv8 network and AI technology
摘要
Abstract
Solder joint failures are a common cause of circuit interruptions in aerospace electronic equipment,and artificial intelligence(AI)technology provides a new approach for real-time and accurate fault detection.In this study,an improved defect detection model named YOLO-KSD is proposed based on the YOLOv8 architecture.An adaptive optimization strategy was incorporated,which included a Kernel Warehouse(KW)backbone,a lightweight GSConv+Slim Neck module,and a Dynamic Head framework,to enhance feature extraction efficiency and detection adaptability.Several public solder joint and circuit board defect datasets were referenced and combined with actual PCB images to construct an expanded defect dataset for training and validating the AI model.The results indicated that,under identical hardware conditions,the YOLO-KSD model significantly improved the detection accuracy for PCB solder joint and board defects.The average processing time per frame was reduced by approximately 11.1%.The model effectively handled various defect types,with particularly notable improvements in small-object detection.This study lays a foundation for advancing the detection of solder joint defects in aerospace electronic equipment.关键词
YOLOv8网络/AI模型优化/数据集构建/缺陷检测/小目标检测Key words
YOLOv8/AI model optimization/dataset construction/defect detection/small object detection分类
通用工业技术引用本文复制引用
苏亮,田子涵,徐凯建,张东胜,方舟..基于YOLOv8网络和AI技术的航天电子设备故障诊断方法研究[J].航天器环境工程,2025,42(5):549-556,8.基金项目
中央高校人才基金项目(编号:buctrc202026) （编号:buctrc202026）
