Analyzing a series of thermal infrared images to identify defects using a hybrid approach that combines robust principal component analysis and image segmentation

Thermography is widely used to identify defects; however, analyzing just the images containing important information instead of analyzing each individual image from a series of thermal infrared images is an important capability. Principal component analysis (PCA) is the traditionally used dimensionality reduction approach for projecting a given dataset onto a low-dimensional space. In doing so, several images can be extracted from this low-dimensional space that contain significant information about the given data. The problem with this is that PCA is sensitive to noise and shadows. Robust PCA (RPCA), Sparse PCA (SPCA), and Kernel PCA (KPCA) have therefore been developed and employed for defect detection. The question then arises as to which method can preserve the most information from a series of thermal infrared images after the data are projected onto the low-dimensional space. This study analyzed a series of thermal infrared images covered with shadows using different PCAs. By comparing the results, it was found that RPCA can preserve the most information of the given data. Image segmentation was employed to segment the extracted images such that the thermal patterns could be easily identified from the segmented regions. The shadow locations could be identified by selecting the areas whose shadow effects estimated using image segmentation were less than 1.0. The results were encouraging and the proposed scheme can be feasibly employed for defect detection. •The proposed scheme combines the Robust Principal Component Analysis (RPCA) and image segmentation to efficiently identify defects.•The processed results using PCA, Sparse PCA, Kernel PCA, and Robust PCA are compared, and it found that RPCA can preserve more information of the given data than other methods do.•The areas of shadows can be identified by setting the estimated shadow effects as 1.0.