A Deep Ensemble Classifier for Surface Defect Detection in Aircraft Visual Inspection

2020 ◽  
Vol 4 (1) ◽  
pp. 20200031
Author(s):  
Ivan Ren ◽  
Feraidoon Zahiri ◽  
Gregory Sutton ◽  
Thomas Kurfess ◽  
Christopher Saldana
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defect ◽  
Ensemble Classifier ◽  
Surface Defect Detection

scholarly journals Surface Defect Detection of Nonburr Cylinder Liner Based on Improved YOLOv4

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yongbin Chen ◽  
Qinshen Fu ◽  
Guitang Wang
Keyword(s):  
Internal Combustion Engine ◽  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defect ◽  
Combustion Engine ◽  
Cylinder Liner ◽  
Internal Combustion ◽  
Detection Algorithm ◽  
Model Framework ◽  
Surface Defect Detection

Cylinder liner plays an important role in the internal combustion engine. The surface defects of cylinder liner will directly affect the safety and service life of the internal combustion engine. At present, the surface defect detection of cylinder liner mainly relies on manual visual inspection, which is easily affected by subjective factors of inspectors. Aiming at the bottleneck of traditional visual inspection technology in appearance inspection, this paper proposes a surface defect detection algorithm based on deep learning to realize defect location and classification. Based on the characteristics of the research object in this paper, the surface defect detection algorithm based on the improved YOLOv4 model is proposed, the model framework is constructed, and the data enhancement method and verification method are proposed. Experiments show that the proposed method can improve the detection accuracy and speed and can meet the requirements of the nonburr cylinder surface defect detection. At the same time, the method can be extended to other surface defect detection applications.

Positive-Sample-Based Surface Defect Detection Using Memory-Augmented Adversarial Autoencoders

2021 ◽  
pp. 1-1
Author(s):  
Tongzhi Niu ◽  
Bin Li ◽  
Weifeng Li ◽  
Yuanhong Qiu ◽  
Shuanlong Niu
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Positive Sample ◽  
Surface Defect Detection

SDDNet: A Fast and Accurate Network for Surface Defect Detection

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Lisha Cui ◽  
Xiaoheng Jiang ◽  
Mingliang Xu ◽  
Wanqing Li ◽  
Pei Lv ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

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