COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.

Deep learning-based methodologies are a key component towards the goal of autonomous driving. For a successful application, these models require a significant amount of training data, which is difficult, time-consuming, and expensive to collect. This study assesses the effectiveness of Generative Adversarial Networks (GANs) in generating high-quality training images for in-vehicle applications using a limited dataset. Two advanced GAN architectures were compared for their ability to produce realistic in-vehicle RGB images. The results showed that the StyleGAN-ADA outperformed the MSG-GAN, generating images with better fidelity and accuracy, making it more suitable for scenarios with limited data. However, challenges such as mode collapse and long training times, particularly for high-resolution images, were identified. The models’ reliance on the quality and diversity of the training dataset also limits their effectiveness in real-world applications. This research highlights the potential of GANs to reduce the lack of data in autonomous driving, pointing to future approaches for optimizing these models.