scholarly journals Surface Defect Detection and Recognition Method for Multi-Scale Commutator Based on Deep Transfer Learning

2021 ◽  
Author(s):  
Yufeng Shu ◽  
Bin Li
Keyword(s):  
Transfer Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Recognition Method ◽  
Multi Scale ◽  
Surface Defect Detection ◽  
Detection And Recognition

scholarly journals Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature

2021 ◽  
Vol 11 (22) ◽  
pp. 10508
Author(s):  
Chaowei Tang ◽  
Xinxin Feng ◽  
Haotian Wen ◽  
Xu Zhou ◽  
Yanqing Shao ◽  
...  
Keyword(s):  
High Resolution ◽  
Defect Detection ◽  
Automobile Industry ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
The Body ◽  
Multi Scale ◽  
Surface Defect Detection ◽  
Edge Features ◽  
Automobile Wheel

Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.

scholarly journals TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples

2021 ◽  
Author(s):  
Jiahui Cheng ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Guangzhi Wu ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

Surface Defect Detection Using U-net and transfer learning

2020 ◽  
Author(s):  
Xiaojun Xia ◽  
Shuai Wang ◽  
Shanshan Liu ◽  
Lanqing Ye ◽  
Binbin Yang
Keyword(s):  
Transfer Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

scholarly journals An Efficient Network for Surface Defect Detection

2020 ◽  
Vol 10 (17) ◽  
pp. 6085 ◽  
Author(s):  
Zesheng Lin ◽  
Hongxia Ye ◽  
Bin Zhan ◽  
Xiaofeng Huang
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Characteristic Curve ◽  
Detection Performance ◽  
Training Dataset ◽  
Multi Scale ◽  
Surface Defect Detection ◽  
Cascade Structure ◽  
Asymmetric Loss Function ◽  
Roc Score

Convolutional neural networks (CNN) have achieved promising performance in surface defect detection recently. Although many CNN-based methods have been proposed, most of them are limited by the few samples available for training, and the imbalance of positive and negative samples. Hence, their detection performance needs to be further improved. To this end, we propose a multi-scale cascade CNN called MobileNet-v2-dense to detect defects more efficiently. Specifically, the multi-scale cascade structure used in our network can help capture the weak defect semantics that may be lost in the deep network. Then, we propose a novel asymmetric loss function to further improve detection performance. Lastly, a two-stage augmentation method effectively enlarges the training dataset. Experimental results show that, compared to the state-of-the-art, the area under the receiver-operating characteristic curve (AUC-ROC) score of our method increased by 0.16.

Nickel foam surface defect detection based on spatial-frequency multi-scale MB-LBP

2019 ◽  
Vol 24 (8) ◽  
pp. 5949-5957 ◽  
Author(s):  
Bin-fang Cao ◽  
Jian-qi Li ◽  
Nao-sheng Qiao
Keyword(s):  
Spatial Frequency ◽  
Defect Detection ◽  
Surface Defect ◽  
Nickel Foam ◽  
Multi Scale ◽  
Surface Defect Detection

scholarly journals Potato Surface Defect Detection Based on Deep Transfer Learning

Agriculture ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 863
Author(s):  
Chenglong Wang ◽  
Zhifeng Xiao
Keyword(s):  
Transfer Learning ◽  
Defect Detection ◽  
Surface Defect ◽  
Fine Tuning ◽  
Final Model ◽  
Out Of Sample ◽  
Surface Defect Detection ◽  
Sample Testing ◽  
Overall Performance ◽  
Speed And Accuracy

Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato surface defect detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models—SSD Inception V2, RFCN ResNet101, and Faster RCNN ResNet101—on a self-developed dataset, and achieved an accuracy of 92.5%, 95.6%, and 98.7%, respectively. RFCN ResNet101 presented the best overall performance in detection speed and accuracy. It was selected as the final model for out-of-sample testing, further demonstrating the model’s ability to generalize.

Surface defect detection of cylindrical lithium-ion battery by multiscale image augmentation and classification

2021 ◽  
pp. 2140011
Author(s):  
Harshad K. Dandage ◽  
Keh-Moh Lin ◽  
Horng-Horng Lin ◽  
Yeou-Jiunn Chen ◽  
Kun-San Tseng
Keyword(s):  
Lithium Ion Battery ◽  
Defect Detection ◽  
Surface Defect ◽  
Classification Scheme ◽  
Lithium Ion ◽  
Multi Scale ◽  
Image Patches ◽  
Stage Classification ◽  
Defect Image ◽  
Surface Defect Detection

While deep convolutional neural networks (CNNs) have recently made large advances in AI, the need of large datasets for deep CNN learning is still a barrier to many industrial applications where only limited data samples can be offered for system developments due to confidential issues. We thus propose an approach of multi-scale image augmentation and classification for training deep CNNs from a small dataset for surface defect detection on cylindrical lithium-ion batteries. In the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery images is used in the learning process of a two-stage classification scheme that aims to differentiate defect image patches of lithium-ion batteries in the first stage and to identify specific defect types in the second stage. The LSDD approach is an efficient prototyping method of defect detection from limited training images for quick system evaluation and deployment. The experiments show that, based on only 26 source images, the proposed LSDD (i) constructs two augmented multi-scale datasets of 19,309 and 6889 image patches for training and test, respectively, (ii) achieves 93.67% accuracy for discriminating defect image patches in the first stage, and (iii) reaches 90.78% mean precision rate and 93.89% mean recall rate for defect type identification in the second stage. Our two-stage classification scheme has higher defect detection sensitivity than an intuitive one-stage classification scheme by 0.69%, and outperforms the one-stage scheme in identifying specific defect types. For comparing with YOLOv3 detector, less defect misdetections are observed in our approach as well.

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