Object Detection and Localisation in Thermal Images by means of UAV/Drone.

Object detection is one of the crucial tasks that has made deep learning of fundamental importance in last years, also thanks to the use of drones and unmanned aerial vehicles able to obtain images and videos in real-time from any location. In the absence of daylight or artificial light for object detection, it is necessary to resort to thermal images, obtained by converting infrared radiation (i.e., heat) into visible images that depict the spatial distribution of temperature differences in a scene viewed by a thermal camera. However, object detection in this type of image and video stream is still challenging due to the complicated scene information and coarse resolution compared to a visible image or video. In this paper, we propose a method aimed to detect objects in thermal images, in particular, the proposed method is aimed to identify persons and dogs acquired from a thermal camera installed, for instance, on drones or unmanned aerial vehicles. We employ an object detection model, i.e., the "You only look once" one, for the automatic localization of objects in thermal images. In the evaluation of a dataset composed of 203 images with 257 annotations, the proposed method obtains a precision of 0.897, a recall equal to 0.904, and a mean Average Precision value (with an Intersection over Union greater than 0.5) equal to 0.924, showing the effectiveness of the proposed method for the identification and location of persons and dogs from images directly acquired with thermal cameras. [ABSTRACT FROM AUTHOR]