Enhancing Object Detection in Adverse Conditions using Thermal Imaging
This work addresses improved object detection for autonomous driving systems, but it appears incremental as it builds on existing fusion methods and datasets.
The paper tackled object detection in adverse conditions by augmenting visible spectrum images with thermal sensors, demonstrating that thermal images significantly improve detection accuracy in nighttime imagery.
Autonomous driving relies on deriving understanding of objects and scenes through images. These images are often captured by sensors in the visible spectrum. For improved detection capabilities we propose the use of thermal sensors to augment the vision capabilities of an autonomous vehicle. In this paper, we present our investigations on the fusion of visible and thermal spectrum images using a publicly available dataset, and use it to analyze the performance of object recognition on other known driving datasets. We present an comparison of object detection in night time imagery and qualitatively demonstrate that thermal images significantly improve detection accuracy.