Multi-spectral fusion using generative adversarial networks for UAV detection of wild fires

Wild fires are now increasingly responsible for immense ecological damage. Unmanned aerials vehicles (UAVs) are being used for monitoring and early-detection of wild fires. Recently, significant research has been conducted for using Deep Learning (DL) vision models for fire and smoke segmentation. Such models predominantly use images from the visible spectrum, which are operationally prone to large false-positive rates and sub-optimal performance across environmental conditions. In comparison, fire detection using infrared (IR) images has shown to be robust to lighting and environmental variations, but long range IR sensors remain expensive. There is an increasing interest in the fusion of visible and IR images since a fused representation would combine the visual as well as thermal information of the image. This yields significant benefits especially towards reducing false positive scenarios and increasing robustness of the model. However, the impact of fusion of the two spectrum on the performance of fire segmentation has not been extensively investigated. In this paper, we assess multiple image fusion techniques and evaluate the performance of a U-Net based segmentation model on each of the three image representations - visible, IR and fused. We also identify subsets of fire classes that are observed to have better results using the fused representation.European Union funding: 77830

Advancing fault diagnosis in aircraft landing gear: an innovative two-tier machine learning approach with intelligent sensor data management

Revolutionizing aircraft safety, this study unveils a pioneering two-tier machine learning model specifically designed for advanced fault diagnosis in aircraft landing gear systems. Addressing the critical gap in traditional diagnostic methods, our approach deftly navigates the challenges of sensor data anomalies, ensuring robust and accurate real-time health assessments. This innovation not only promises to enhance the reliability and safety of aviation but also sets a new benchmark in the application of intelligent machine-learning solutions in high-stakes environments. Our method is adept at identifying and compensating for data anomalies caused by faulty or uncalibrated sensors, ensuring uninterrupted health assessment. The model employs a simulation-based dataset reflecting complex hydraulic failures to train robust machine learning classifiers for fault detection. The primary tier focuses on fault classification, whereas the secondary tier corrects sensor data irregularities, leveraging redundant sensor inputs to bolster diagnostic precision. Such integration markedly improves classification accuracy, with empirical evidence showing an increase from 95.88% to 98.76% post-imputation. Our findings also underscore the importance of specific sensors—particularly temperature and pump speed—in evaluating the health of landing gear, advocating for their prioritized usage in monitoring systems. This approach promises to revolutionize maintenance protocols, reduce operational costs, and significantly enhance the safety measures within the aviation industry, promoting a more resilient and data-informed safety infrastructure