# On the joint use of simulations and experimental RX images for generating very large datasets with application to object detection tasks

Anthony Touron, Roberto Miorelli, Julie Escoda, Edouard Demaldent, Souad Bannouf

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## To cite this version:

Anthony Touron, Roberto Miorelli, Julie Escoda, Edouard Demaldent, Souad Bannouf. On the joint use of simulations and experimental RX images for generating very large datasets with application to object detection tasks. e-Journal of Nondestructive Testing, 2025, 10th International Symposium on Digital Industrial Radiology and Computed Tomography, 30 (8), 10.58286/31442. cea-05516029

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Submitted on 17 Feb 2026

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# On the joint use of simulations and experimental RX images for generating very large datasets with application to object detection tasks

Anthony TOURON<fnref n="1" />*, Roberto MIORELLI<fnref n="1" />*, Julie ESCODA<fnref n="1" />,
Edouard DEMALDENT<fnref n="1" />, Souad BANNOUF<fnref n="2" />

## Abstract

Machine Learning (ML) and Deep Learning (DL) algorithms are increasingly used in industrial domains to enhance accuracy and efficiency in error-prone tasks. Their performance hinges on data quantity and quality. In Nondestructive Testing and Evaluation (NdT&E), data scarcity, often due to confidentiality and rare events, poses a challenge. This is particularly true for Radiographic Testing (RT) data. To address this, we propose a systematic approach to combine simulated data with experimental radiographic (RX) images, creating large datasets with common defects (e.g., porosity, flaws) found in nuclear welding inspections. This process, called in this work Virtual Flaws (VFs) insertion, enables training ML algorithms without manual labelling or extensive experimental datasets. We evaluate the effectiveness of VFs-based datasets in object detection tasks using state-of-the-art algorithms.

**Keywords:** Radiographic Testing, Virtual Flaws, Object Detection, Deep Learning, Computer Vision, RT modelling

## 1. Introduction

The use of Machine Learning (ML) is gathering the attention of Nondestructive testing and Evaluation (NdT&E) and Structural Health Monitoring (SHM) research communities for performing classification and flaw sizing tasks [1] [2] [3]. In particular, the use of Deep Learning (DL) algorithms is a very active field of research across all NdT&E methods. The lack of availability of large dataset for training the most advanced ML algorithms does not enable to exploit the full potential of DL algorithms. To tackle these issues, scholars developed multiple strategies aiming at artificially increasing the dataset size relying on both numerical simulations and experimental acquisitions for time-series and post-processed ultrasonic inspection data [4] [5] [6]. In the context of Radiographic Testing (RT) inspections, pristine experimental RX images have been exploited to generate new simulated data. This has proven to be a straightforward yet effective approach to artificially enhance the number of images data to be used to train ML algorithms [7] [8] [9].

In this context, the paper presents a systematic approach to hybridising simulated data with experimental RX images. This is done in order to generate large datasets that may contain multiple types of defects (e.g., porosity, porosity clusters, transverse/longitudinal flaws, etc.) that can appear in weld inspections in the nuclear industry. In more detail, several parametric models of different flaw topologies have been developed. These models allow flaws' geometrical parameters and positions to be taken into account. We then used the flaw thickness

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<footnote n="1">¹Université Paris-Saclay, CEA, List, F-91190, Palaiseau, France</footnote>

<footnote n="2">²IRSN, Fontenay-aux-Roses, France</footnote>
information extracted from the simulation to embed the flaws' signatures into the pristine
experimental RT acquisition of weld parts, using Beer-Lambert's law. This process, known as
Virtual Flaws (VFs) insertion, forms the foundation of the dataset generation step. The large
dataset obtained can be used to train machine learning (ML) algorithms without the need for
manually labelled data or access to a large experimental dataset. In this work, we analyse the
efficiency of VFs-based datasets to address object detection tasks performed via state-of-the-
art object detection algorithms tested on a set of testing images issued from the GDXray dataset
[7].

## 2. Context and related works

The defect detection and classification of defects in RX images based on deep learning has been
explored by NDT&E scholars in the last decade. Possible approaches consist to use a
conventional network architecture, potentially with modifications, such as UNet (see reference
[10]) or VGG (see [11]). An alternative approach is to use a pre-trained network with transfer
learning (see references [12] and [13]). Other papers employ alternative, less traditional
methodologies. For instance, the authors of [14] employ a self-learning method (unsupervised
learning) for feature representation, followed by a soft-max layer for classification.

Multi-model ensemble is a method that involves applying multiple networks in parallel to
reduce false negatives. This approach is similar to the manner in which various operators
analyse the same image visually (see [13]). It has also been noted that training on both detection
and segmentation tasks simultaneously produces more accurate detection results (see [15]).

Furthermore, other studies have demonstrated the c of various algorithms in detecting defects
in aluminium casting inspections (i.e. the GDX data set). These algorithms, which rely on object
detection tasks in images, have shown to achieve a high degree of accuracy. For example, one
stage object detection algorithm [16] demonstrated a 95% success rate in detecting simulated
virtual ellipsoidal defects embedded in a pristine experimental background. In the study by
Garcia Perez (2022), a RetinaNet model based on ResNet-50 [17] backbone, exploiting a
feature pyramid network [18], has been developed for detecting defects in aluminium casting
based on the GDX data set. A defect recognition strategy, i.e. defect segmentation, has been
applied to a RT data set containing solid propellant geometries based on numerically simulated
virtual defects embedded in the data set [19]. In addition, a virtual defect-based strategy has
been adopted in [20] to create a suitable data set for the training of a U-Net architecture for the
purpose of porosity segmentation in the aerospace domain. In the study by [21], the efficacy of
various object detection algorithms (e.g., YOLO's family implementations, EfficientDet and
RetinaNet) was examined for the purpose of detecting gas holes and shrinkage defects in high-
pressure aluminium die-casting specimens.

## 3. Material and Methods

### 3.1. Overview of the virtual defect(s) insertion procedure developed

CIVA is a comprehensive simulation software dedicated to the Non-Destructive Testing. One
module is focused on radiographic inspection, covering the entire chain from photon emission
to image acquisition for both digital and film-based radiography. A hybrid model that uses a
combination of ray tracing and Monte Carlo methods allows to take into account scatter
contribution, considering interactions such as Compton scattering, Rayleigh scattering, and pair production. The ray-tracing allows an accurate representation of parametric or meshed components, including 3D flaws, within a virtual inspection scene. One of the main innovations in CIVA is the ability to perform virtual flaw insertion, which is essential for generating annotated datasets required for training and validating machine learning algorithms in non-destructive testing. The insertion procedure explained in Figure 2, derived from the methodology proposed by D. Mery [8], is based on the Beer-Lambert law and assumes that the presence of a small defect does not significantly alter the scatter contribution. The model further assumes a single-material specimen, monoenergetic photon source, and linear detector response.

A defect present on the source-detector path results in a modification of the transmitted radiation $\phi(x)$, as shown in Figure 1. Considering the detector response linear, we modify the value of the experimental image as follows, for each pixel corresponding to a flaw region:

$$I(x) = A \cdot \phi(x) + B$$ $$I(x - d) = [I(x) - B] \cdot \exp(\mu d) + B$$

<figure><img src="image_4.png" /><figcaption>Figure 1 – Illustration of the radiation attenuation in the part, with lower attenuation in the presence of a defect.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 2 – Virtual flaws insertion procedure based on the use of numerical simulations.</figcaption></figure>

In practice, flaw insertion requires only two parameters: the linear attenuation coefficient of the specimen in which we insert the flaw (based on material and energy), and a “differential thickness value” related to the pristine image (difference between darkest and brightest pixels in the region of interest). Defects are modelled in 3D and projected into the detector space, creating a local defect thickness mask. This mask is used to locally modify the grey levels in the pristine image, simulating the presence of a flaw. Because the flaw geometry and position are fully controlled, annotations for training data are automatically generated. This method was validated by inserting a virtual flaw into a simulation and comparing the result with the original pristine image as shown in Figure 3. The resulting augmented image preserves the general
characteristics of the pristine image, although the simulated noise is noted to be overly Gaussian and the weld geometries too idealized. This shows the benefit of using real experimental images for more realistic flaw insertion.

<figure><img src="image_6.png" /><figcaption>Figure 3 – Left: Simulated radiographic image; Middle: Augmented image with virtual flaw; Right: Superposed profile from both images.</figcaption></figure>

The upcoming version of CIVA will introduce new types of simulated flaws to enhance realism and broaden the scope of application. These include longitudinal and transverse cracks, modelled using in-house meshing techniques which construct geometries from sequences of slightly misaligned rectangular elements. Clusters of spherical porosities are simulated using a randomized distribution of non-overlapping spheres within a specified 3D volume fraction, while isolated porosities are represented with ellipsoidal shapes. Although lack of penetration and inclusions are not yet implemented, the model's flexibility makes it straightforward to include them in the future using similar geometric representations. These developments significantly advance the capabilities of CIVA for both traditional radiographic simulation and the creation of high-quality synthetic datasets for machine learning applications.

## 3.2. Dataset generation procedure based on virtual flaws

The performance of the object detection algorithm trained on virtual flaws is evaluated by selecting a number of challenging scenarios. The initial step in this process is to consider a small subset of the welds from the GDXRay dataset. Consequently, regardless of whether a smooth or textured pristine weld was used, the training and validation datasets were generated based on all the defects. Each defect type was assigned a class ID ranging from 0 to 3. It is important to acknowledge the potential similarity between the classes of porosity and cluster of porosity, particularly in instances where the number of porosity points is minimal. In Table 1, we provide the information about training, validation and test sets based on the use of VFs.

<table><caption>Table 1 – Samples generated by using the VFs implementation based on pristine weld parts from GDXRay dataset welds group series W0003 [22].</caption><thead><tr><th>Dataset</th><th>Pristine Weld (Smooth)</th><th>Pristine Weld (Textured)</th><th>Dataset Smooth Welds</th><th>Dataset Textured Welds</th><th>Dataset Mixed-Welds</th></tr></thead><tbody><tr><td>Training</td><td rowspan="4">W003[000106, 000113, 000125, 000154, 000161]</td><td rowspan="4">W003[0005, 0009, 00057]</td><td>21870</td><td>18468</td><td>40338</td></tr><tr><td>Validation</td><td>2430</td><td>2052</td><td>4482</td></tr><tr><td>Test</td><td>2700</td><td>2280</td><td>4980</td></tr><tr><td>All</td><td>27000</td><td>22800</td><td>49800</td></tr></tbody></table>
In particular, referring to Table 1, in order to incorporate virtual flaws into pristine RT images, it is essential to select representative backgrounds. In order to guarantee that the training set is as divergent as possible from the test set, the number of pristine welds used for VFs insertion is restricted by considering a subset of the GDXRay dataset [23]. Furthermore, in order to assess the impact of different backgrounds on detection performance, both smooth and textured welds are included in the study. As illustrated in Figure 4, the pristine welds, which exhibit both smooth and textured surfaces, are used for the purpose of embedding VFs. It is evident that there is a significant difference between smooth and textured welds. It should be noted that in some cases, these texture patterns may be subject to misinterpretation, which could potentially lead to imperfections such as porosity or fine cracks. This is the main reason why these two types of welds were used for training and cross-data framework for the testing phase.

To accommodate the inclusion of a large test dataset, which allows for greater variability in both welds and flaws, the generation of 90 testing images for smooth welds and 37 images for the textured was achieved. Moreover, in order to evaluate the efficiency of the algorithm in relation to IQI or other RT image indicators, a set of 37 images was extracted from the GDXRay dataset. It is hypothesised that there remains scope for improvement in the quantity of extracted images from GDXRay. However, given the more complex nature of the welds, it would be preferable to engage the services of a RT expert to ensure that the selection and analysis of the images is carried out effectively and in line with the desired outcomes.

In Figure 4, we provide some examples of the virtual defect insertion procedure as analysed in this work. It can be noted that the subtle details of defects such as shading effects of cracks signature e.g., along the crack length are preserved along with the main weld texture regardless of the background considered. Furthermore, it should be noted that the bounding boxes are automatically generated (i.e., see, Figure 2) and can therefore be easily adjusted in size to accommodate tailored training needs.

<figure><img src="image_7.png" /><figcaption>Figure 4 – Examples of augmented images with virtual flaws with red rectangular bounding box.</figcaption></figure>

### 3.3. On the choice of the ML algorithm

In the context of object detection, deep learning-based tasks, two families of object detection algorithms are referred to as two- and one-stage algorithms. These algorithms are used to assess the performance of different deep learning architectures once applied to the detection of defects in industrial manufacturing. Two-stage object detection algorithms rely on an object detection pipeline. This involves generating region proposals and then performing a subsequent classification stage on each proposal region. The classification stage divides the object categories. In the field of two-stage algorithms, the most notable examples are R-CNN, Fast R-CNN and Faster R-CNN [24]. The one-stage counterpart is a method of approaching the object
detection task that involves the solution of a regression and classification problem. It employs
a unified framework to achieve category and location predictions directly [25]. Two of the most
well-known algorithms are the SSD and YOLO family algorithms [26].

YOLO based algorithms [27] rely on the use of a single deep convolutional neural network working on the whole image. Toward this end, the algorithm considers adjacent regions in the image and predicts the bounding boxes and the probabilities for each of the regions. More into details, YOLO takes as input an image and it outputs a vector containing the probability of the class associated to an object that is framed by a bounding box described by four scalars. The remaining elements of the vector depend on the number of classes considered for training. In case of multiple candidate boxes are retrieved, the YOLO algorithm retains the bounding boxes having the highest probability. This paper relies on the use of YOLO version 8 implementation of the algorithm [27].

**3.3.1. Training performance evaluation**

Evaluation methods are necessary to quantify the obtained results. For that purpose, comparison between the proposed network and other networks is classically used (see [10] and [12]). Classical metrics from classification are used, such as precision, recall or accuracy (see [10]). Those are based on the false positive, false negative, true positive and true negative measurements.

In the case of detection tasks, state-of-the-art algorithm such as YOLO or Faster R-CNN [25] use Average Precision (AP) as evaluation criterion. This measurement is based on the IoU (Intersection over Union), which is defined, for two given shapes, as the ratio between the intersection area and the union area (see Figure 5). Following this definition, two perfectly overlaid boxes have IoU = 1.

<figure><img src="image_8.png" /><figcaption>Figure 5 – Left: Two shapes (green and red) being compared. Middle: Intersection in blue. Right: Union in blue.</figcaption></figure>

When testing a detection method, IoU is computed between detected object, and the ground
truth of the database. A threshold parameter α is chosen, and a detected box is considered a
correct detection if its value of IoU is such as IoU > α. From a set of detections, we can then
define:

* True Positive (TP): detected boxes intersecting a ground truth box with *IoU* > *α*,

* False Positive (FP): detected boxes intersecting a ground truth box with IoU < α, and detected boxes not intersecting a ground truth box,
* False Negative (FN): ground truth boxes intersecting a detected box with IoU < α, and ground truth boxes not intersecting a detected box.

* False Negative (FN): ground truth boxes intersecting a detected box with IoU < α, and ground truth boxes not intersecting a detected box.

These values let us define precision as *precision* = $\frac{TP}{TP+FP}$, and recall as *recall* = $\frac{TP}{TP+FN}$.
## 4. Numerical and experimental results assessment

The main purpose of this work was about studying the feasibility of exploiting VFs to generate large labelled dataset for training object detection algorithms. Therefore, our objective was not to achieve the best possible performance from the trained model but rather to assess the feasibility of the studied methodology. That is, we preferred to employ directly an off-the-shelf parametrization of YOLO algorithm as baseline and qualitatively understand what is the impact of the experimental weld background on the detection outcomes.

Figure 6 contains the detection results of YOLO algorithms trained on different weld backgrounds as referred in Table 1. These qualitative results show that the algorithm trained on a dataset containing VFs is able to detect unseen real defect in both textured and smooth welds. Since detection was performed on parts of welds that do not belong to the pristine experimental backgrounds used for VFs insertion it is believed that the constitution of large training set (see Table 1) lead the model to have an overall good ability to generalise.

<figure><img src="image_9.png" /><figcaption>Figure 6 – Example of the detections obtained via a YOLO model trained on a dataset containing VFs only. In a), VFs were embedded into experiment RX images smooth with smooth texture, in b) textured-only welds were accounted and in c) both textured and smooth experimental RX images were used.</figcaption></figure>
From the computational point of view, the YOLO model was trained in approximatively 30 minutes on Pentium Xeon Gold 5220R with 128GB RAM and a GPU based on an Nvidia RTX A5000 card with 24GB RAM. The test phase is almost-real time (i.e., ~100 ms/image).

# 5. Discussion

The qualitative validation demonstrated the feasibility of generating a training dataset based on pristine RT images containing multiple VFs. It has been determined that it is preferable to consider the greatest possible diversity in the set of RT images used as the pristine background. It is not possible to provide an accurate assessment of the minimum number of RT pristine images due to the high level of variability observed in the RT images within the GDXRay dataset. This variability manifests in a variety of forms, including but not limited to texture, shape, resolution, and scale. It is hypothesised that the complexity of such a study could be mitigated considering a particular inspection configuration.

In creating the training dataset with VFs, the focus was on a specific category of flaws and a general, parametric description. The present approach may have certain disadvantages. From the perspective of VFs generation, it has been determined that a more detailed examination of the defect family and the associated defect descriptions to be retrieved in the online phase could potentially improve the overall performance of the object detection algorithms. The findings of the present analysis demonstrate that the majority of undetected and partial detections were associated with flaw topologies that were not sufficiently accounted for during the training process. These include volumetric longitudinal cracks across the entire image, large and elongated porosity clusters, and so on.

# 6. Conclusions

This work presents an analysis on a seamless procedure for adding VFs real background RX images that can be employed to artificially augment the size of the training set in both class representation and number of training samples. Our findings demonstrate that the approach becomes more robust when an a-priori analysis is conducted on the targeted flaws. This is not expected to be a significant issue, as the morphology and type of defects to be inspected are typically known in advance and a parametric description of their morphology is thus known. In this paper, the full potential of the a priori information was not realized due to the lack of available data. Consequently, our objective was to create a VFs based dataset that would allow us to test the developed methodology on a wider range of cases by using a less accurate description of the flaws.

The studied object detection algorithms, i.e. YOLOv8, showed high computational performance with minimal impact on the overall RT analysis time retaining good qualitative performances. This enabled the interactive analysis of acquisitions with on-threshold parameters. It is supposed that the use of large datasets based on the use of the hybrid procedure presented in this work can be profitable for other object detection algorithms too (e.g., alternative YOLO's family algorithms as well as faster RCNN, etc.).
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