Object Detection in Monocular Infrared Images Using Classification – Regresion Deep Learning Architectures

The rapid development of deep learning architectures that have a good performance on object detection in visual monocular images has triggered and interest towards the application of these architectures on other image modalities such as stereovision or infrared images.We propose a framework for multi-class object detection in monocular infrared images that integrates and compares different classification-regression deep learning architectures [1] on a novel benchmark infrared dataset developed by FLIR.The work described is evaluated using standard object detection metrics and an average precision of 82% for pedestrians, 86% for cars and 66% for bicycles is achieved while running at 40fps.