# Pulsed Thermography Dataset for Training Deep Learning Models
## 1 Introduction  
Pulsed thermography (PT) has gained widespread popularity as a Non-Destructive Evaluation (NDE) method in the past few years, due to its high accuracy and speed. It is especially suitable for detecting near-surface defects in materials such as metal, Carbon Fiber Reinforced Polymers (CFRP), and Polyvinyl Chloride (PVC). Its application spans several industries, including aerospace and automotive. However, the high complexity of PT data, which often contains noise, artifacts, and inconsistencies, makes its inspection challenging. Therefore, the application of deep learning methods in processing PT data is an important research topic. By using deep learning models, the efficiency and accuracy of defect detection within the PT data can be significantly improved. This is crucial for ensuring the reliability and safety of various engineering systems. Thus, the development of accurate and efficient deep-learning-based techniques for PT data processing can greatly enhance the capabilities of NDE for various industrial applications. In recent years, deep learning algorithms have made remarkable progress in various image processing tasks such as image classification [1, 2, 3], object detection [4, 5, 6], image segmentation [7, 8, 9], and video understanding [10, 11]. To evaluate the performance of these algorithms, commonly used publicly available datasets, such as ImageNet [12], PASCAL [13], COCO [14] for image classification and Cityscapes [15], ADE20K [16] for image segmentation, play a crucial role. These datasets serve as benchmarks for different deep learning methods. However, collecting such datasets in the NDE field presents severalchallenges. One of the main challenges is dealing with imbalanced datasets [17], where the majority of components being tested are free of defects or damage, while only a small proportion of them are defective. This can render an artificial intelligence (AI) model ineffective if proper data preprocessing is not conducted. In addition to the imbalanced dataset challenge, the limited availability of data is another challenge in the NDE field. In particular, obtaining PT data is more difficult than obtaining normal images, as it requires specialized equipment and expertise to perform experiments in a dedicated laboratory. Furthermore, the labeling of PT data presents a multi-faceted challenge. Accurately analyzing PT data and labeling it requires skilled expertise. Additionally, validating the absence or presence of defects can be a challenging task, as defects are often hidden beneath the surface of the object. Achieving accurate validation may require destructive testing or CT scanning, both of which can be costly or damaging to the testing object. In the field of NDE, deep learning methods for data segmentation have been applied to defects defection [18; 19; 20]. However, access to publicly available datasets is often limited due to internal regulations. This paper addresses this issue by providing a well-designed yet relatively simple PT dataset that can facilitate future research in evaluating AI algorithms. The dataset can be found at [[https://www.kaggle.com/datasets/ziangwei/irtpvc](https://www.kaggle.com/datasets/ziangwei/irtpvc)]([https://www.kaggle.com/datasets/ziangwei/irtpvc](https://www.kaggle.com/datasets/ziangwei/irtpvc)) (accessed on 23 February 2023). Additionally, we have experimented with several popular deep learning models for instance segmentation on this dataset. In summary, this work makes several contributions. Firstly, a publicly available and carefully designed PT dataset for PVC components with defects of various sizes and depths is developed. This dataset can serve as a benchmark for training deep learning models and further research in the field. Secondly, the study includes experiments and results comparison using several popular encoder-decoder based defect segmentation models on the developed dataset.