Smoke Detection

Smoke detection research focuses on developing accurate and efficient methods for identifying smoke in various contexts, from wildfires to industrial settings and even hand-held actions like smoking. Current efforts leverage deep learning, particularly convolutional neural networks (CNNs) and transformer-based architectures, often incorporating multimodal data fusion (e.g., combining visual and LiDAR data) to improve robustness and accuracy. These advancements are crucial for enhancing safety in diverse applications, ranging from search and rescue operations in hazardous environments to improved environmental monitoring and industrial process control. The development of larger, more diverse datasets is also a key area of focus, enabling the training and evaluation of more sophisticated algorithms.