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Ultrasonic testing (UT) is a commonly used approach for inspection of material and defect detection without causing harm to the inspected component. To improve the reliability of defect detection, the material is often scanned from various angles leading to an immense amount of data that needs to be analyzed. Some of the defects are only seen on B-scans taken from a particular angle so discarding some of the data would increase the risk of not detecting all of the defects. Recently there has been significant progress in the development of methods for automated defect analysis from the UT data. Using such methods the inspection can be performed quicker, but it is still necessary to inspect all of the angles to detect defects. In this work, we test a novel approach for accelerating the analysis by merging the images from various angles. To reduce the information loss during the process of merging, we develop a new model with a weighting module that dynamically determines the importance of each of the scanning angles. Using the proposed module, the loss of information is minimal, so the precision of the detection model is comparable to the model tested on each of the images separately. Using the merged images input, the analysis can be accelerated by almost 15 times.

Non-destructive ultrasonic analysis of materials is a method for assessing the integrity of the inspected components. It is commonly used in monitoring critical parts of the power plants, in aeronautics, oil and gas, and the automotive industry. Since most ultrasonic inspections rely on expert's previous experience they must constantly practice on new, unseen data. Acquiring enough data for training human experts on non- destructive ultrasonic scan analysis can be an expensive and time-consuming task. The only possibility to get new data for practicing is to implant synthetic defects in real metal blocks. Artificial defects are made by temperature strain, electrical discharge, and physical damage. All of those methods are very complicated and expensive to perform. Also metal blocks have to be taken from the components of the power plants to have the same structure and be realistic. In this work, some attempts have been made to generate 3D ultrasonic scans using computer vision and deep learning methods.

The analysis of the data in non-destructive ultrasonic testing of materials is a very time- intensive task. To alleviate the aforementioned strain on the human expert inspectors, a plethora of assisted analysis methods based on deep learning have been developed recently. However, most of these methods are based on the automated detection of flaws in A-scans and B-scans and therefore we propose a method based on the detection of flaws in C-scans that can reduce the complexity of manual detection of flaws in B- scans. The proposed method classifies each row of the C-scan based on whether it contains any flaws or not. Afterward, the positively classified rows are forwarded for further automated (and manual) inspection. The results show that the developed method significantly reduces the number of B-scans that have to be further analyzed.

Non-destructive testing is a set of techniques for defect detection in materials. While the set of imaging techniques is manifold, ultrasonic imaging is the one used the most. The analysis is mainly performed by human inspectors manually analyzing the acquired images. A low number of defects in real ultrasonic inspections and legal issues concerning data from such inspections make it difficult to obtain proper results from automatic ultrasonic image (B-scan) analysis. The goal of presented research is to obtain an improvement of the detection results by expanding the training data set with realistic synthetic samples. In this paper, we present a novel deep learning Generative Adversarial Network model for generating realistic ultrasonic B-scans with defects in distinct locations. Furthermore, we show that generated B-scans can be used for synthetic data augmentation, and can improve the performances of deep convolutional neural object detection networks. Our novel method was developed on a dataset with almost 4000 images and more than 6000 annotated defects. When trained only on real data, detector can achieve an average precision of 70%. By training only on generated data the results increased to 72%, and by mixing generated and real data we achieve almost 76% average precision. We believe that synthetic data generation can generalize to other tasks with limited data. It could also be used for training human personnel.

Analysis of ultrasonic testing (UT) data is a time-consuming assignment. In order to make it less demanding we propose an approach based on a variational autoencoder (VAE) to filter out the scans without anomalies/defects and in doing so, partially automate the procedure. The implemented approach uses an additional encoder network allowing to encode the reconstructed images. The differences in encodings of input and reconstructed images have shown to be good indicators of anomalous data. Anomaly detection results surpass the results of other VAE based anomaly criteria.

Ultrasonic imaging is widely used for non-destructive evaluation in various industry applications. Early detection of defects in materials is the key to keeping the integrity of inspected structures. Currently, there have been some attempts to develop models for automated defect detection on ultrasonic data. To push the performance of these models even further more data is needed to train deep convolutional neural networks. A lot of data is also needed for training human experts. However, gathering a sufficient amount of data for training is a challenge due to the rare occurrence of defects in real inspection scenarios. This is why inspection results heavily depend on the inspector’s previous experience. To overcome these challenges, we propose the use of Generative Adversarial Networks for generating realistic ultrasonic images. To the best of our knowledge, this work is the first one to show that a Generative Adversarial Network is able to generate images indistinguishable from real ultrasonic images. The most thorough statistical quality analysis to date of generated ultrasonic images has been conducted with the participation of human expert inspectors. The experimental results show that images generated using our Generative Adversarial Network provide the highest quality images compared to other published methods.

Nondestructive evaluation (NDE) is a set of techniques used for material inspection and defect detection without causing damage to the inspected component. One of the commonly used nondestructive techniques is called ultrasonic inspection. The acquisition of ultrasonic data was mostly automated in recent years, but the analysis of the collected data is still performed manually. This process is thus very expensive, inconsistent, and prone to human errors. An automated system would significantly increase the efficiency of analysis, but the methods presented so far fail to generalize well on new cases and are not used in real-life inspection. Many of the similar data analysis problems were recently tackled by deep learning methods. This approach outperforms classical methods but requires lots of training data, which is difficult to obtain in the NDE domain. In this work, we train a deep learning architecture EfficientDet to automatically detect defects from ultrasonic images. We showed how some of the hyperparameters can be tweaked in order to improve the detection of defects with extreme aspect ratios that are common in ultrasonic images. The proposed object detector was trained on the largest dataset of ultrasonic images that was so far seen in the literature. In order to collect the dataset, six steel blocks containing 68 defects were scanned with a phased-array probe. More than 4000 VC-B-scans were acquired and used for training and evaluation of EfficientDet. The proposed model achieved 89.6% of mean average precision (mAP) during fivefold cross validation, which is a significant improvement compared to some similar methods that were previously used for this task. A detailed performance overview for each of the folds revealed that EfficientDet-D0 successfully detects all of the defects present in the inspected material.

Non-destructive ultrasonic testing (UT) of materials is used for monitoring critical parts in power plants, aeronautics, oil and gas industry, and space industry. Due to a vast amount of time needed for a human expert to perform inspection it is practical for a computer to take over that task. Some attempts have been made to produce algorithms for automatic UT scan inspection mainly using older, non-flexible analysis methods. In this paper, two deep learning based methods for flaw detection are presented, YOLO and SSD convolutional neural networks. The methods' performance was tested on a dataset that was acquired by scanning metal blocks containing different types of defects. YOLO achieved average precision (AP) of 89.7% while SSD achieved AP of 84.5 %.