Hotspot Detection

Hotspot detection involves identifying areas of elevated activity or concentration, whether it's thermal anomalies in electronic circuits or equipment, viral transmission in crowds, pollution levels in urban environments, or biological diversity in coral reefs. Current research focuses on leveraging machine learning, particularly object detection models like YOLO and RetinaNet, often enhanced with techniques such as PCA-guided augmentation or physics-informed neural networks (PINNs), to improve accuracy and efficiency. These advancements have significant implications for various fields, enabling more effective monitoring of infrastructure, disease spread, environmental hazards, and resource management in complex systems.