This work deals with a different problem: the determination of the size of defects from the resulting inspection using active thermography. The authors propose a simple yet effective image processing pipeline based on linear operations and blob analysis. Tests are performed using a PVC plate that has two flat-bottomed defects artificially fabricated at different depths. Additional tests are performed with a CFRP specimen. The proposed method offers an automated approach for defect detection, with applications to quality assurance. In [11], another research work is presented about detection capacity in active thermography. This work proposes a novel data analysis methodology that extracts a greater amount of information from the recorded sequences by applying advanced processing techniques. The proposed work presents a novel approach for defect detection: the extracted information is synthesized into three channels that may be represented through real color images and processed by quaternion algebra techniques. The colorization of thermographic images is a well-known method that can provide enhanced defect detectability. The novelty in this work is the application of quaternion analysis to enhance the detection capability. To validate the proposed approach, synthetic data and actual experimental data are used. Results are quantitatively compared using different signal-to-noise ratio metrics. Compared with other methods, such as approximate polynomial sequences and their first and second derivatives as well as principal component analysis, the quaternionic processing techniques produce higher SNR values. In some real defects, the first derivative provides better results, but in general, the proposed methodology improves results compared to conventional thermographic processing. Moreover, it improves the distinction of defects due to their representation in different color shades. This work also concludes that the choice of SNR definition to assess the results has a great impact on the analysis. An interesting industrial application of infrared thermography is presented in [12]. This work uses active infrared thermography to inspect rails. Inductive thermography is used in this case, as it can identify surface cracks very effectively. Two different approaches are considered: stop-and-go and line-scan. The former method takes several images of the rail and creates a single, combined panorama image. The latter scans the rail continuously while the rail is moving forward along the track. The two inspection approaches are analyzed and compared, underscoring the advantages and disadvantages for each of them. In addition, different image processing tools are proposed to fully characterize the rail defects. The research work also includes finite element simulations to further analyze the problem and to optimize the experiments. Testing concludes that inductive thermography is an excellent nondestructive testing method for localizing surface defects in metallic materials. Further research work is planned, but tests with different specimens show very promising results. The last paper of this special issue presents a methodology to optimize dynamic line-scan thermography [13]. The authors of this work include Prof. [PERSON], one of the most prolific and influential researchers in the field of infrared thermography. Dynamic line-scan thermography is a relatively new inspection method, which requires a complex parameter tuning procedure that mostly depends on the material's properties. The research work presents an optimization approach using the response surface that can give more insights about the interconnections between parameters. Throughout the paper, the creation of the response surface is discussed and how it can be used for the optimization, offering an explanation for the influence of every variable used. Tests are performed on a PVC flat-bottom hole plate.