摘要
为实现航空电连接器自动化焊接的精准定位,提出一种基于机器学习的航空电连接器焊杯剖面检测与图像分割方法。通过增加小目标检测层、CBAM机制和采用GhostNet网络,提高原始网络模型的特征提取有效性和预测准确率,同时降低改进后的模型参数量和空间大小。实验结果表明,改进YOLOv5s-Seg模型的平均精度均值为84.2%和44.6%。相较于YOLOv5s原模型,平均精度均值分别提升5.5%和1.3%。所提出的检测与分割方法能较好地实现精度与速度的平衡,有利于实际应用和设备部署,为改进基于机器视觉的航空电连接器自动化焊接提供一定的理论基础。
To achieve the accurate positioning of automated welding of aviation electrical connector(AEC),a method for the detection and segmentation of welding cup profiles was proposed based on machine learning.The effectiveness of feature extraction and prediction accuracy of the original network model were enhanced by incorporating a small target detection layer,the CBAM mechanism,and the GhostNet network.Concurrently,the number of parameters and space size of the improved model were reduced.The experimental results show that the improved YOLOv5s-Seg model achieves mean average precision of 84.2%and 44.6%.Compared with the original YOLOv5s model,this represents improved by 5.5%and 1.3%,respectively.The detection-segmentation method proposed effectively balances precision and speed,facilitating practical application and equipment deployment,and provides a theoretical basis for advancing the automated welding of AEC based on machine vision.
作者
张洪溥
刘喜艳
赵峰志
闫希研
冯艳
ZHANG Hongpu;LIU Xiyan;ZHAO Fengzhi;YAN Xiyan;FENG Yan(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Shanghai Large-Scale Component Intelligent Manufacturing Robot Technology Collaborative Innovation Center,Shanghai 201620,China)
出处
《上海工程技术大学学报》
2025年第3期366-374,共9页
Journal of Shanghai University of Engineering Science
基金
上海地方高校能力建设项目(23010501600)。
关键词
机器学习
深度学习
航空电连接器
图像分割
machine learning
deep learning
aviation electrical connector(AEC)
image segmentation
