Defect Detection Scheme of Pins for Aviation Connectors Based on Image Segmentation and Improved RESNET-50

IF 0.8 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING International Journal of Image and Graphics Pub Date : 2022-12-15 DOI:10.1142/s0219467824500116
Hailong Yang, Yinghao Liu, Tian Xia
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引用次数: 1

Abstract

In this paper, a new detection method of pin defects based on image segmentation and ResNe-50 is proposed, which realizes the defect detection of faulty pins in many aviation connectors. In this paper, a new dataset image segmentation method is used to segment many aviation connectors in a single image to generate a dataset, which reduces the tedious work of manually labeling the dataset. In the defect detection model, based on ResNet-50, a ResNet-B residual structure is introduced to reduce the loss of features during information extraction; a continuously differentiable CELU is used as the activation function to reduce the neuron death problem of ReLU; a new deformable convolution network (DCN v2) is introduced as the convolution kernel structure of the model to improve the recognition of aviation connectors with prominent geometric deformation pin recognition. The improved model achieved 97.2% and 94.4% accuracy for skewed and missing pins, respectively, in the experiments. The detection accuracy improved by 1.91% to 96.62% compared to the conventional ResNet-50. Compared with the traditional model, the improved model has better generalization ability.
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基于图像分割和改进RESNET-50的航空连接器引脚缺陷检测方案
本文提出了一种新的基于图像分割和ResNe-50的引脚缺陷检测方法,实现了许多航空连接器中故障引脚的缺陷检测。本文采用一种新的数据集图像分割方法,在单个图像中分割多个航空连接器以生成数据集,减少了手动标记数据集的繁琐工作。在缺陷检测模型中,基于ResNet-50,引入了ResNet-B残差结构,以减少信息提取过程中特征的丢失;使用连续可微的CELU作为激活函数来减少ReLU的神经元死亡问题;引入了一种新的可变形卷积网络(DCN v2)作为模型的卷积核结构,以提高具有显著几何变形引脚识别的航空连接器的识别能力。在实验中,改进的模型对偏斜和缺失引脚的准确率分别达到97.2%和94.4%。与传统的ResNet-50相比,检测准确率提高了1.91%至96.62%。与传统模型相比,改进后的模型具有更好的泛化能力。
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来源期刊
International Journal of Image and Graphics
International Journal of Image and Graphics COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
18.80%
发文量
67
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