Vision-Based Damage Detection in CFRP Beams Using Optical Flow and Mahalanobis-Enhanced Deep Learning Models

Abstract

This study presents a novel vision-based methodology for damage detection in CFRP composite beams, combining optical flow analysis, statistical anomaly scoring, and deep learning (DL) models. Composite materials such as CFRP are widely used in structural applications due to their high strength-to-weight ratio, yet detecting internal damage remains a significant challenge. To address the limitations of traditional non-destructive evaluation methods, this study integrates non-contact optical flow techniques with a hybrid anomaly detection pipeline. The Lucas-Kanade optical flow method is used to extract displacement time series from video recordings of vibrating structures. These displacement signals are transformed into spectrograms using Short-Time Fourier Transform (STFT), and frequency-domain features are enhanced with added Gaussian noise to improve model robustness. Principal Component Analysis (PCA) is applied to reduce the dimensionality of the spectrogram features, and Mahalanobis Distance is computed to quantify deviations from the healthy state. The resulting Mahalanobis Distance time series is then used as input for three DL architectures—Autoencoder, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—which are trained to detect structural anomalies based on reconstruction error or pattern recognition. The proposed approach is experimentally validated on CFRP composite beams under multiple damage scenarios. Results show that leveraging Mahalanobis-based statistical features within DL models significantly improves anomaly detection accuracy, offering a robust and scalable framework for real-time structural health monitoring in civil, aerospace, and automotive domains.