基于改进YOLO的航空电连接器焊杯剖面图像分割方法

张洪溥; 刘喜艳; 赵峰志; 闫希研; 冯艳

doi:10.12299/jsues.24-0125

基于改进YOLO的航空电连接器焊杯剖面图像分割方法

doi: 10.12299/jsues.24-0125

张洪溥^{1, 2,},
刘喜艳^{1, 2},
赵峰志^{1, 2},
闫希研^{1, 2},
冯艳^{1, 2, ,}

1.
上海工程技术大学机械与汽车工程学院, 上海 201620
2.
上海大型构件智能制造机器人技术协同创新中心, 上海 201620

基金项目: 上海地方高校能力建设项目（23010501600）

详细信息

作者简介:
张洪溥（1994 − ），男，硕士生，研究方向为机器视觉、机器学习。E-mail：171881271@qq.com

通讯作者:
冯　艳（1976 − ），女，教授，博士，研究方向为机器视觉、光纤智能感知等。E-mail：fyan@sues.edu.cn

中图分类号: TP391.41
计量
- 文章访问数: 17
- HTML全文浏览量: 10
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-29
- 网络出版日期: 2025-12-22
- 刊出日期: 2025-09-30

Improved YOLO-based image segmentation method for AEC welding cup profile

ZHANG Hongpu^{1, 2
,},
LIU Xiyan^{1, 2},
ZHAO Fengzhi^{1, 2},
YAN Xiyan^{1, 2},
FENG Yan^{1, 2
, ,}

1.
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Shanghai Large-Scale Component Intelligent Manufacturing Robot Technology Collaborative Innovation Center, Shanghai 201620, China

摘要

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。
- 机器学习 /
- 深度学习 /
- 航空电连接器 /
- 图像分割
Abstract: 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.
- machine learning /
- deep learning /
- aviation electrical connector (AEC) /
- image segmentation

HTML全文

图 1 改进后的YOLOv5s-Seg模型

Figure 1. Improved YOLOv5s-Seg model

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图 2 Ghost瓶颈层原理图

Figure 2. Schematic diagram of Ghost bottleneck layer

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图 3 Ghost模块原理图

Figure 3. Schematic diagram of Ghost module

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图 4 通道注意力结构

Figure 4. Structure of channel attention

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图 5 空间注意力结构

Figure 5. Structure of spatial attention

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图 6 模型损失曲线

Figure 6. Model loss curves

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图 7 焊杯剖面识别分割效果对比

Figure 7. Comparison of weld cup profile identification and segmentation effect

下载: 全尺寸图片幻灯片

表 1 硬件配置

Table 1. Hardware configuration

环境参数	配置
中央处理器	I7-13700KF
显卡型号	GeForce3090
运行内存	64 GB
显卡内存	24 GB
操作系统	Windows10
深度学习框架	Pytorch2.0.1
Cudnn	Cudnn11.8
OpenCV	4.8

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表 2 训练参数表

Table 2. Training parameters

模型参数	配置
初始学习率	0.01
输入图像大小	640 × 640
优化器	SGD
训练周期	300
批量大小	16
动量	0.937
权重衰减率	0.0005
学习率衰减方式	余弦退火算法

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表 3 不同注意力机制模型精度实验结果

Table 3. Experimental results on the accuracy of different attention mechanism models

编号	模型	预测率/%	召回率/%	F₁/%	${{\mathrm{mAP}}_{50}}$/%	${{\mathrm{mAP}}_{50 - 95}}$/%
0	+ CBAM	88.3	75.3	80.7	84.2	44.6
1	+ SimAM	90.3	72.5	80.4	80.7	42.7
2	+ ECA	91.4	71.4	80.2	81.8	43.5
3	+ SE	87.8	72.0	80.2	79.1	43.7

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表 4 不同骨干网络模型精度实验结果

Table 4. Experimental results of different backbone network model accuracy

编号	模型	预测率/%	召回率/%	F₁/%	${{\mathrm{mAP}}_{50}}$/%	${{\mathrm{mAP}}_{50 - 95}}$/%
0	+ 4P-CBAM	90.9	72.5	80.7	83.5	44.8
1	+ ShuffleNetV2	86.8	67.1	75.7	77.2	40.2
2	+ MobileNetV3	87.8	74.5	80.6	79.2	41.7
3	+ RepGhost	89.5	72.3	80.0	81.8	42.6

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表 5 不同骨干网络模型速度实验结果

Table 5. Experimental results on speed of different backbone network models

编号	模型	层数	参数量	浮点运算量	模型大小/MB	FPS
1	+ 4P-CBAM	356	7.51×10⁶	54.0×10⁹	15.77	93.5
2	+ ShuffleNetV2	340	3.70×10⁶	44.0×10⁹	8.43	94.3
3	+ MobileNetV3	356	3.13×10⁶	47.3×10⁹	7.24	83.3
4	+ GhostNet	300	5.57×10⁶	48.8×10⁹	12.03	88.5
5	+ RepGhost	647	4.02×10⁶	44.4×10⁹	9.17	77.5

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表 6 不同模型精度实验结果

Table 6. Experimental results of different model accuracy

编号	模型	预测率/%	召回率/%	F₁/%	${{\mathrm{mAP}}_{50}}$/%	${{\mathrm{mAP}}_{50 - 95}}$/%
0	YOLOv5s-G	88.3	75.3	81.3	84.2	44.6
1	YOLOv5s-Seg	90.9	71.2	79.8	78.7	43.3
2	YOLOv7s-Seg	93.9	70.7	80.7	78.4	43.3
3	YOLOv8s-Seg	91.8	69.5	79.1	78.3	42.1

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表 7 不同模型速度实验结果

Table 7. Experimental results of different model speeds

编号	模型	层数	参数量	浮点运算量	模型大小/MB	FPS
0	YOLOv5s -G	300	5.57×10⁶	48.8×10⁹	12.04	88.5
1	YOLOv5s-Seg	165	7.40×10⁶	25.7×10⁹	14.86	90.0
2	YOLOv7s-Seg	325	9.52×10⁶	35.9×10⁹	19.13	130.0
3	YOLOv8s-Seg	261	11.80×10⁶	42.7×10⁹	23.31	151.0

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