Shooting distance estimation from a gunshot residue pattern (GSR) is traditionally performed using chemical and optical methods. Infrared thermography offers a non-destructive alternative for visualising GSR patterns on textile substrates. This work proposes a data-driven approach to estimating shooting distance by analysing thermographic images with a convolutional neural network (CNN). Using kurtosis images were selected as a compact, high-contrast representation of thermographic sequences. A large experimental dataset covering a wide range of shooting distances, firearms, and ammunition types was analyzed. Several CNN architectures were evaluated using transfer learning and fine-tuning. A multimodal architecture combining image features with categorical ballistic information was also investigated. A VGG-based model achieved the most reliable performance, while the multimodal model showed overfitting, suggesting potential for larger, more diverse datasets. Model explainability was addressed through error analysis and Grad-CAM visualisations, confirming that the networks focus on physically meaningful regions. The proposed approach demonstrates the potential of combining infrared thermography with deep learning for reliable, robust forensic estimation of shooting distance.
A quantifier of the thermal response to a thermal challenge in the lower extremities can reveal peripheral impairment in patients with type 2 Diabetes Mellitus (DM2), aiding timely diagnosis and treatment. This study introduces the parameter V-ri as a feasible quantifier of metabolic performance in a temperature regulation process of the lower limbs of individuals with DM2. This index is derived from the analysis of an infrared image sequence through the rate of recovery of metabolic heat of the lower limbs after an instantaneous thermal stimulus, termed the slope value index. The same thermal challenge is applied to a group of patients with DM2 and control volunteers, also clinically supervised. Evaluation of V-ri showed reduced recovery performance in patients with DM2 compared to the performance exhibited by the control group. According to ROC analysis, this parameter V-ri yielded sensitivity and specificity above 0.8, reaching 92% and 83%, respectively. An additional result is that distance V-ri shows that its correlation with clinical variables is statistically significant: glucose (p < 0.01) and HbA1c (p < 0.001). This method offers a non-radiative and non-invasive approach for assessing metabolic deterioration in the lower extremities, potentially suitable for unlimited use both in diagnosis and during treatment.
Ensuring railway safety is a paramount concern, particularly in detecting overheated axle bearings, commonly referred to as hot boxes. In an attempt to improve upon existing operational solutions, the integration of fast infrared (IR) thermal vision technology in the wayside with computer vision has provided new insights for automatically detecting hot-bearing boxes. However, this method involves labour-intensive frame-by-frame processing that can delay critical decision-making. Instead, the present research work shifts focus to the processing of panoramic IR thermal images. Our approach begins with the study and development of a pre-processing technique that employs coarse-to-fine optical flow estimation and image stitching to generate high-resolution panoramic views from sequential IR frames. To enable automatic detection and counting of the hot box, two distinct detection approaches are implemented: custom image processing approach and deep-learning based computer vision approach. The performance of both approaches is evaluated across diverse experimental scenarios, involving both freight and passenger trains and using two distinct IR camera models, cooled and uncooled. Preliminary results are encouraging, demonstrating a clear reduction in the time required for hot box detection and counting, thereby enhancing overall train monitoring efficiency.
