This paper presents a thermal imaging dataset from composite material samples (carbon and glass fiber reinforced plastic) that were inspected by pulsed thermography with the goal of detecting and characterizing subsurface defective zones (Teflon inserts representing delaminations between plies). The pulsed thermography experiment was applied to 6 academic plates (inspected from both sides) all having the dimensions of 300 mm x 300 mm x 2 mm and same distribution of defects but made of different materials: three plates on carbon fiber-reinforced plastic (CFRP) and three plates made on glass fiber reinforced plastic (GFRP) specimens with three different geometries: planar, curved and trapezoidal. Each plate contains 25 inserts having length/depth ratios between 1.7 and 75. Two FX60 BALCAR photographic flashes (6.2 kJ per flash) were used to generate the heat pulse (2 ms duration), an X6900 FLIR infrared camera using ResearchIR software to record the thermal images and a custom-built software/control unit to synchronize data recording with pulse generation. Finally, the dataset proposed consists of 12 sequences of approximately 2000 images of 512 × 512 pixels each.

Thermographic image analysis is a subfield of diagnostic image processing aimed at detecting breast abnormalities in women at an early stage. It is a developing field of research and its effectiveness and scope require scientific assessment to be determined. An open-access dataset has been created for the scientific community to test and develop techniques for computational detection of normal and abnormal breast conditions from thermograms. This dataset is a valuable resource for researchers due to the scarcity of publicly available datasets of breast thermographic images. It includes thermographic images of the female chest area in three capture positions: anterior, left oblique and right oblique. The data set comes from 119 women ranging from 18 to 81 years of age. A table is attached to the dataset with the diagnosis of breast pathology, showing that 84 patients had benign pathology and 35 patients had malignant pathology. The diagnoses of women with healthy breast pathology are not included.

This paper presents a new methodology for the automatic detection of defective regions of interest (d-ROI) in thermal images of composite materials. The images are acquired with pulsed thermography, and local histograms of oriented gradients are obtained by thermogram processing. This information is analyzed using a simple strategy to differentiate the material background from the defective areas. The procedure is independent of image contrast or enhancement; it does not require analysis of a complete sequence of images, nor does it involve heat transfer models or the extraction of nonuniform heating information. The methodology is tested with synthetic images of a carbon fiber-reinforced plastic sample, containing diameter/depth ratio defects with different values (between 150 and 0.56). The performance of the d-ROI detection method is validated using the area under the ROC curve (AUC) measure, generally obtaining a maximum average value of 0.949 with variations between 0.891 and 0.993 for all the defective depth and size conditions studied. In addition, this method is highly robust when detecting defects in 48.84% of the total number of images, as determined by the sequences analyzed with AUC values higher than 0.95. Outside the high detectability index range, the AUC performance increases abruptly and decays gradually. Recent literature proposes automatic detection of defects in thermograms yielding similar performances to those obtained with the proposed method; however, they require preprocessing of all the thermograms to improve image contrast and visibility and to attenuate the adverse effect of nonuniform heating, which affects the implementation complexity and the computational cost.

This article presents a dataset of thermal and visible aerial images of the same flat scene at Melendez campus of Universidad del Valle, Cali, Colombia. The images were acquired using an UAV equipped with either a thermal or a visible camera. The dataset is useful for testing techniques for the improvement, registration and fusion of multi-modal and multi-spectral images. The dataset consists of 30 visible images and their metadata, 80 thermal images and their metadata, and a visible georeferenced orthoimage. The metadata related to every image contains the WGS84 coordinates for allocating the images. Also, the homography matrices between every image and the orthoimage are included in the dataset. The images and homographies are compatible with the well-known assessment protocol for detection and description proposed by Mikolajczyk and Schmid [1].