[1O1-GS-7] Vision, speech media processing: GAN

[1O1-GS-7-04] A Data Augmentation Technique Using CycleGAN for Object Detection on Far-Infrared Images Robust to Ambient Temperature

〇Masaya Hojo¹, Kota Yoshida¹, Takeshi Fujino¹ (1. Ritsumeikan University)

Keywords:DNN, GAN, Far-Infrared Image, Data Augmentation

The object detection technology using deep neural networks (DNNs) is used in advanced driver assistance systems (ADAS). A visible-light (RGB) camera is often used in automobiles, however performance-degradation occurs because of weak reflected light from objects when the ambient light source is insufficient. A far-infrared (FIR) camera, which detects FIR light radiated from objects, is not affected by the ambient light sources. On the other hand, it is required to collect FIR images in various seasons and weather because the appearance of objects on FIR images depends on the ambient temperature. Furthermore, FIR cameras are not commonly equipped with vehicles, making it difficult to acquire large numbers of FIR images. In this paper, we generate FIR images from RGB images by using multiple Cycle GAN trained at different ambient temperatures. The object detection model trained by the generated images has high robustness against the change of ambient temperature.