Breast cancer is one of the most common and lethal types of cancer worldwide, with millions of new cases diagnosed each year. Early detection is pivotal in improving patient outcomes and significantly increases the chances of successful treatment. While traditional detection methods such as mammography are effective, they can be invasive, costly, painful, and less applicable for younger women with denser breast tissue. In this context, infrared thermography emerges as a promising, non-invasive technique for breast cancer detection. However, analyzing these images presents challenges due to noise and irrelevant information that can interfere with accurate diagnosis. In this work, we propose a method for segmenting infrared breast images using the DeepLabV3+ Convolutional Neural Network (CNN). Our approach leverages the power of deep learning to precisely delineate breast regions, enabling more accurate feature extraction for subsequent classification tasks. Results achieved an average accuracy of 98.69%, an Intersection over Union (IoU) of 97.18%, and a precision of 98.48%, demonstrating a clear improvement over previous approaches, particularly in terms of segmentation quality, making our method a robust tool for enhancing automated breast cancer detection.

Convolutional neural networks (CNNs) have shown great potential in different real word application. Defining a suitable CNN architecture is vital for obtaining good performance. In this work we propose a random forest surrogate combined with two bio-inspired optimization algorithm, genetic algorithms (GA) and particle swarm optimization (PSO) used to find good CNN fully connected layer architecture and hyperparameters for three state of the art CNNs: VGG-16, Resnet-50 and Densenet-201. The proposed model is used to classify breast thermography images from the DMR-IR database in order to find whether or not the patient has cancer. The proposed model improved F1-score from 0.92 to 1 for the Densenet using the GA and also Resnet from 0.85 of F1-score to 0.92 using the PSO. Moreover, the surrogate model also helped reducing training time.

Breast cancer is a disease that affects many women throughout the world. It is the second most common type of cancer. The early diagnosis of the disease is relevant for increasing the chances of the patient recovering. Thermography is a promising technique that might be used to help the early diagnosis of breast cancer. In this work, we use three state of the art CNNs (VGG-16, Densenet201, and Resnet50) combined with transfer learning to classify static thermography images (sick and healthy). In our experiments, the best results have an F1-score of 0.92, 91.67% for accuracy, 100% for sensitivity, and 83.3% for specificity obtained with the Densenet using 38 static images for each class.

Pulsed thermography is one of the most popular thermography inspection methods. During an experiment of pulsed thermography, a specimen is quickly heated, and infrared images are captured to provide information about the specimen’s surface and subsurface conditions. Adequate transformations are usually performed to enhance the contrast of the thermal images and to highlight the abnormal regions before these thermal images are visually inspected. Given that deep neural networks have been a success in computer vision in the past few years, a data contrast enhancement approach with stacked denoising autoencoder (DAE) is proposed in this paper to enhance the abnormal regions in the thermal frames gathered by pulsed thermography. Compared to the direct principal component thermography, the proposed method can enhance the abnormalities evidently without weakening important details.

In this paper, optical and mechanical excitation thermography was used to investigate basalt-fiber-reinforced polymer, carbon-fiber-reinforced polymer, and basalt-carbon fiber hybrid specimens subjected to impact loading. Interestingly, two different hybrid structures including sandwich-like and intercalated stacking sequence were used. Pulsed phase thermography, principal component thermography, and partial least-squares thermography (PLST) were used to process the thermographic data. X-ray computed tomography was used for validation. In addition, signal-to-noise ratio analysis was used as a means of quantitatively comparing the thermographic results. Of particular interest, the depth information linked to Loadings in PLST was estimated for the first time. Finally, a reference was provided for taking advantage of different hybrids in view of special industrial applications.

The society's concern about safety is growing every day and with it the demand for intelligent surveillance systems with the minimal human intervention possible. In this work we identify suspicious events that could take place in a parking lot based on infrared imagery. The object segmentation process is performed using a dynamic background-subtraction technique which robustly adapts detection to illumination changes. Segmented objects are tracked by a two phase function: prediction and correction. During the tracking process the objects are classified into two categories: Person and Vehicles, based on features like size, velocity and temperature. With the objects correctly segmented and classified using features like velocity and time stood in one spot, it is possible to identify suspicious events occurring in the monitored area. Experimental results are presented to demonstrate the effectiveness of the proposed technique to recognize suspicious events.