Face Detection In Thermal Images With Yolov3

The automotive industry is currently focusing on automation in their vehicles, and perceiving the surroundings of an automobile requires the ability to detect and identify objects, events and persons, not only from the outside of the vehicle but also from the inside of the cabin. This constitutes relevant information for defining intelligent responses to events happening on both environments. This work presents a new method for in-vehicle monitoring of passengers, specifically the task of real-time face detection in thermal images, by applying transfer learning with YOLOv3. Using this kind of imagery for this purpose brings some advantages, such as the possibility of detecting faces during the day and in the dark without being affected by illumination conditions, and also because it's a completely passive sensing solution. Due to the lack of suitable datasets for this type of application, a database of in-vehicle images was created, containing images from 38 subjects performing different head poses and at varying ambient temperatures. The tests in our database show an AP50 of 99.7% and an AP of 78.5%.