Automatic anomaly detection from X-ray images based on autoencoders

Presenti, Alice; Liang, Z.; Pereira, Luis F. Alves; Sijbers, Jan; Beenhouwer, Jan De

doi:10.1080/10589759.2022.2074415

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…In industrial circles, X-ray is used to detect material defects or unwanted elements in other objects, which allows the detection of anomalies in the entire volume, not just on the surface. Among the works that use autoencoders for these purposes, it is worth mentioning [ 49 ], in which the quality of metal details is examined. However, this work is based on artificial data.…”

Section: Related Workmentioning

confidence: 99%

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Kocon,

Malesa,

Rapcewicz

2023

Sensors

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Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product’s properties, it lowers customers’ confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries.

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Section: Related Workmentioning

confidence: 99%

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Kocon,

Malesa,

Rapcewicz

2023

Sensors

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“…Tokime et al used a deep learning segmentation algorithm with a SegNet network architecture for the segmentation of pores in x-ray images of welds [20]. Presenti et al developed a defect detection algorithm using a combination of an autoencoder and convolutional neural network for the defect detection directly in the x-ray projection domain [21]. While the previous references prove the ability to directly detect defects and pores in the x-ray projection domain, those methods are not directly applicable for pore detection in laser sintered parts.…”

Section: Introductionmentioning

confidence: 99%

Direct voxel classification from x-ray projections for 3D pore detection applied to laser sintered parts

Bellens,

Guerrero,

Janssens

et al. 2024

Meas. Sci. Technol.

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X-ray Computed Tomography (XCT) is a validated and frequently used tool to verify part geometry and to perform a non-destructive inspection of additive manufacturing parts. However, the acquisition of a large number of X-ray projections generates long inspection times. This conflicts with a high throughput of the production process and hinders the integration of XCT as an in-line quality control procedure for low-end parts. In this paper, we propose a method to obtain 3D information of the internal pores from a limited view or limited angle scan. The method combines a forward projection model of a cone-beam X-ray system and a deep learning neural network to directly classify each individual voxel, based on the X-ray projections in order to avoid the reconstruction and segmentation step. Accompanying reconstruction artefacts for limited view and limited angle XCT scans are thereby reduced, while preserving 3D information of the pores, defects or inclusions inside the material. The method is validated on real X-ray projections of polymer laser sintered industrial parts and shows a significant reduction in the required X-ray projections, hence acquisition time.

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“…Studies on such sparse models have indicated both promising results ( [27] and accuracy of 100% for welds), as well as some discouraging ( [13], accuracy of 73% for aluminum casting data). Recently, DL-based approaches have started to attract attention, such as in [28] (the authors focused on industrial X-ray CT data) and industrial X-ray inspections of die-casts in [29]; both utilized autoencoders to detect anomalies in X-ray images.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, many of the earlier studies showed promising results with high accuracies on test data similar to training data. However, very few [28,29] of the studies have explicitly addressed how the algorithms react when subjected to unexpected new input data far from the training distribution, so-called out-of-distribution (OOD) data. In principle, dictionary learning and sparse coding should be able to detect OOD data, though there has been very limited explicit exploration.…”

Section: Introductionmentioning

confidence: 99%

Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data

Lindgren

Zach

2022

Metals

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X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence (e.g., deep learning) have been proposed as solutions. However, typically, such solutions are overconfident when subjected to new data far from the training data, so-called out-of-distribution (OOD) data; we claim that safe automatic interpretation of industrial X-ray images, as part of quality control of critical products, requires a robust confidence estimation with respect to OOD data. We explored if such a confidence estimation, an OOD detector, can be achieved by explicit modeling of the training data distribution, and the accepted images. For this, we derived an autoencoder model trained unsupervised on a public dataset with X-ray images of metal fusion welds and synthetic data. We explicitly demonstrate the dangers with a conventional supervised learning-based approach and compare it to the OOD detector. We achieve true positive rates of around 90% at false positive rates of around 0.1% on samples similar to the training data and correctly detect some example OOD data.

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Automatic anomaly detection from X-ray images based on autoencoders

Cited by 13 publications

References 12 publications

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Direct voxel classification from x-ray projections for 3D pore detection applied to laser sintered parts

Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data

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