Anomaly Detection Through Deep Feature Extraction for Automatic Defect Detection in Quadratic Frequency Modulated Thermal Wave Imaging Naga Prasanthi Yerneni, V. S. Ghali, M. N. Swapna, G. T. Vesala Russian Journal of Nondestructive Testing, 2025 Abstract Thermographic data is highly class-imbalanced and scarce while considering the temporal thermal profiles for automatic defect detection using deep learning. Training a supervised deep learning model requires a significantly equal amount of data. Unsupervised deep learning with one-class classification approaches has recently been introduced in thermography for composite inspection. This article proposes an autoencoder-driven anomaly detection model for automatic defect detection in quadratic frequency modulated thermography. The proposed model utilizes the pretrained stacked denoising convolution autoencoder (SDCAE) to extract deep features and feed them to a local outlier factor (LOF) for defect detection. This work analyzes the performance of the proposed SDCAE-LOF on a quick-responsive mild steel specimen with artificially embedded defects of various sizes at different depths. The performance is compared with the CNN-based deep anomaly detection model and other autorncoder models using multiple metrics to confirm the superior defect detection capability of the proposed method.
Enhanced defect detection in thermography through temporal denoising and deep feature extraction using a shallow convolution autoencoder Y Naga Prasanthi, V S Ghali, G T Vesala, Fei Wang, B Suresh Insight Non Destructive Testing and Condition Monitoring, 2025 Various stimulation mechanisms and supportive processing techniques promote infrared thermography (IRT) as a powerful tool for inspecting various industrial objects, such as composites, metals and coatings. Frequency-modulated thermography (FMT) is one stimulation scheme offering superior depth scanning and resolution capabilities. However, noise and non-uniform thermal backgrounds make defect detection challenging in infrared thermography. Recent post-processing advancements following NDT 4.0 adapt machine learning and deep learning-based techniques to automate and enhance defect detection procedures. This study offers a shallow convolution autoencoder (SCAE) in FMT with one-dimensional convolution layers to reduce noise in temporal thermal evolution and train high-level features, resulting in improved defect signatures. This method focuses on creating the denoised temporal thermal response at the decoder end and extracting high-level thermal features from the latent space. Experiments were carried out on a mild steel specimen and a carbon fibre-reinforced polymer (CFRP) specimen embedded with flaws of varying depths and sizes. Compared to the shallow autoencoder (SAE) and stacked deep autoencoder (SDAE), the suggested SCAE efficiently denoised thermal profiles, allowing for improved fault signatures. Furthermore, the latent features of the SCAE produced superior defect signatures compared to the above autoencoder models and traditional dimensionality reduction methods such as principal component analysis (PCA), random projection transform (RPT) and partial least-squares regression (PLSR). The thermal profile and defect signalto-noise ratio (SNR) demonstrate the advantages of the proposed method for improved defect detection.
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