Thermal Image Dataset

Thermal image datasets are crucial for developing and evaluating computer vision algorithms that process infrared imagery, enabling applications across diverse fields like autonomous driving, security, and environmental monitoring. Current research focuses on creating large-scale, diverse datasets for various tasks, including object detection, semantic segmentation, and landmarking, often employing deep learning models such as U-Net and YOLO variants. These datasets address the scarcity of publicly available thermal data, facilitating advancements in algorithms robust to challenging conditions like varying weather and lighting, ultimately improving the accuracy and reliability of thermal imaging applications.