A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images

Gao, Hongbin; Zhang, Ya; Lv, Wenkai; Yin, Jiawei; Qasim, Tehreem; Wang, Dongyun

doi:10.3390/app12136569

Cited by 21 publications

(10 citation statements)

References 23 publications

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“…DCGAN verifies that discriminators can be used for feature extraction in supervised learning tasks and generators can be used for semantic vector computation. Literature [59], [60], [61] all introduced DCGAN to improve its own network and methods, which improved the accuracy and robustness of defect detection.…”

Section: B Common Variantsmentioning

confidence: 99%

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Chang²,

Zhang³

et al. 2022

IEEE Access

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Section: B Common Variantsmentioning

confidence: 99%

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

Chang²,

Zhang³

et al. 2022

IEEE Access

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“…DCGANs are also used in the mechanical and electrical engineering industry. Gao et al used DCGAN for generating images of small industrial parts (gear face) [ 64 ]. DCGAN-generated images were used with traditional data enhancement methods (flip, pan, rotation), which increased the training data size for the CNN.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

Bajić

Orel

Habijan

2022

Sensors

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Charts are often used for the graphical representation of tabular data. Due to their vast expansion in various fields, it is necessary to develop computer algorithms that can easily retrieve and process information from chart images in a helpful way. Convolutional neural networks (CNNs) have succeeded in various image processing and classification tasks. Nevertheless, the success of training neural networks in terms of result accuracy and computational requirements requires careful construction of the network layers’ and networks’ parameters. We propose a novel Shallow Convolutional Neural Network (SCNN) architecture for chart-type classification and image generation. We validate the proposed novel network by using it in three different models. The first use case is a traditional SCNN classifier where the model achieves average classification accuracy of 97.14%. The second use case consists of two previously introduced SCNN-based models in parallel, with the same configuration, shared weights, and parameters mirrored and updated in both models. The model achieves average classification accuracy of 100%. The third proposed use case consists of two distinct models, a generator and a discriminator, which are both trained simultaneously using an adversarial process. The generated chart images are plausible to the originals. Extensive experimental analysis end evaluation is provided for the classification task of seven chart classes. The results show that the proposed SCNN is a powerful tool for chart image classification and generation, comparable with Deep Convolutional Neural Networks (DCNNs) but with higher efficiency, reduced computational time, and space complexity.

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“…Gao et al. [ 24 ] proposed a small-sample gear surface defect detection method based on deep convolutional GAN and lightweight convolutional neural network, and it could classify defective parts in a single context with a classification accuracy of 98.4%. Li et al.…”

Section: Introductionmentioning

confidence: 99%

EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph

Zhang,

Liu,

Zheng

et al. 2023

Plant Phenomics

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Although deep learning-based fruit detection techniques are becoming popular, they require a large number of labeled datasets to support model training. Moreover, the manual labeling process is time-consuming and labor-intensive. We previously implemented a generative adversarial network-based method to reduce labeling costs. However, it does not consider fitness among more species. Methods of selecting the most suitable source domain dataset based on the fruit datasets of the target domain remain to be investigated. Moreover, current automatic labeling technology still requires manual labeling of the source domain dataset and cannot completely eliminate manual processes. Therefore, an improved EasyDAM_V3 model was proposed in this study as an automatic labeling method for additional classes of fruit. This study proposes both an optimal source domain establishment method based on a multidimensional spatial feature model to select the most suitable source domain, and a high-volume dataset construction method based on transparent background fruit image translation by constructing a knowledge graph of orchard scene hierarchy component synthesis rules. The EasyDAM_V3 model can automatically obtain fruit label information from the dataset, thereby eliminating manual labeling. To test the proposed method, pear was used as the selected optimal source domain, followed by orange, apple, and tomato as the target domain datasets. The results showed that the average precision of annotation reached 90.94%, 89.78%, and 90.84% for the target datasets, respectively. The EasyDAM_V3 model can obtain the optimal source domain in automatic labeling tasks, thus eliminating the manual labeling process and reducing associated costs and labor.

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A Deep Convolutional Generative Adversarial Networks-Based Method for Defect Detection in Small Sample Industrial Parts Images

Cited by 21 publications

References 23 publications

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph

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