Dataset Augmentation with Synthetic Images Improves Semantic Segmentation

Goyal, Manik; Rajpura, Param S.; Bojinov, Hristo; Hegde, Ravi S.

doi:10.1007/978-981-13-0020-2_31

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…For example, semantic networks for image segmentation improve their performance when data augmentation of the training dataset is carried out with synthetic images rendered from 3D models [21]. Specifically, authors report a significant increase in IoU from 52.80% to 55.47% when training the system over the PASCAL 2012 dataset enriched with 100 rendered artificial images.…”

Section: Advances In Training Data Synthesismentioning

confidence: 99%

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

et al. 2021

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This paper describes the application of Semantic Networks for the detection of defects in images of metallic manufactured components in a situation where the number of available samples of defects is small, which is rather common in real practical environments. In order to overcome this shortage of data, the common approach is to use conventional data augmentation techniques. We resort to Generative Adversarial Networks (GANs) that have shown the capability to generate highly convincing samples of a specific class as a result of a game between a discriminator and a generator module. Here, we apply the GANs to generate samples of images of metallic manufactured components with specific defects, in order to improve training of Semantic Networks (specifically DeepLabV3+ and Pyramid Attention Network (PAN) networks) carrying out the defect detection and segmentation. Our process carries out the generation of defect images using the StyleGAN2 with the DiffAugment method, followed by a conventional data augmentation over the entire enriched dataset, achieving a large balanced dataset that allows robust training of the Semantic Network. We demonstrate the approach on a private dataset generated for an industrial client, where images are captured by an ad-hoc photometric-stereo image acquisition system, and a public dataset, the Northeastern University surface defect database (NEU). The proposed approach achieves an improvement of 7% and 6% in an intersection over union (IoU) measure of detection performance on each dataset over the conventional data augmentation.

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Section: Advances In Training Data Synthesismentioning

confidence: 99%

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

et al. 2021

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“…For 2D image data, the learning of classifies from examples is described e.g. or [15]. Learning of classifiers for the case of three-dimensional data has been investigated e.g.…”

Section: Classification and Semantic Segmentationmentioning

confidence: 99%

Classification of Point Clouds with Neural Networks and Continuum-Type Memories

Reitmann

Kudryashova

Jung

et al. 2021

IFIP Advances in Information and Communication Technology

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This paper deals with the issue of evaluating and analyzing geometric point sets in three-dimensional space. Point sets or point clouds are often the product of 3D scanners and depth sensors, which are used in the field of autonomous movement for robots and vehicles. Therefore, for the classification of point sets within an active motion, not fully generated point clouds can be used, but knowledge can be extracted from the raw impulses of the respective time points. Attractors consisting of a continuum of stationary states and hysteretic memories can be used to couple multiple inputs over time given non-independent output quantities of a classifier and applied to suitable neural networks. In this paper, we show a way to assign input point clouds to sets of classes using hysteretic memories, which are transferable to neural networks.

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“…Ros et al [31] generated a large set of synthetic images by rendering from a virtual city. Goyal et al [32] also derived synthetic images using 3D models and real images. However, neither method is suitable for the defect inspection study.…”

Section: B Cnn-based Image Segmentation and Defect Detectionmentioning

confidence: 99%

Defect Detection and Classification by Training a Generic Convolutional Neural Network Encoder

Dong

Taylor

Cootes

2020

IEEE Trans. Signal Process.

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Non-destructive Testing (NDT) often involves analysing images to identify (rare) defects. We propose a method for locating and classifying abnormalities using Convolutional Neural Networks (CNNs). A particular problem is that it is often difficult to get large numbers of examples of images of defects, making training a classifier challenging. To address this problem we generate large numbers of synthetic images by combining real defects with different backgrounds. These images are used to train a U-Net style network to perform defect detection at the pixel level. We also demonstrate that the encoder of the network produces features which can be applied to the defect classification task at the image level. Both the defect detection and classification modules are tested on multiple small data sets. Our results show that these modules are able to fulfil the industrial component inspection task at the pixel level (locating defect regions) and image level (identifying if an image contains a defect).

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Dataset Augmentation with Synthetic Images Improves Semantic Segmentation

Cited by 7 publications

References 27 publications

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

Generative Adversarial Networks to Improve the Robustness of Visual Defect Segmentation by Semantic Networks in Manufacturing Components

Classification of Point Clouds with Neural Networks and Continuum-Type Memories

Defect Detection and Classification by Training a Generic Convolutional Neural Network Encoder

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