A Robust Pedestrian and Cyclist Detection Method Using Thermal Images

Computer vision techniques have been frequently applied to pedestrian and cyclist detection for the purpose of providing sensing capabilities to autonomous vehicles, and delivery robots among other use cases. Most current computer vision approaches for pedestrian and cyclist detection utilize RGB data alone. However, RGB-only systems struggle in poor lighting and weather conditions, such as at night, or during fog or precipitation, often present in pedestrian detection contexts. Thermal imaging presents a solution to these challenges as its quality is independent of time of day and lighting conditions. The use of thermal imaging input, such as those in the Long Wave Infrared (LWIR) range, is thus beneficial in computer vision models as it allows the detection of pedestrians and cyclists in variable illumination conditions that would pose challenges for RGB-only detection systems. In this paper, we present a pedestrian and cyclist detection method via thermal imaging using a deep neural network architecture. We have evaluated our proposed method by applying it to the KAIST Pedestrian Benchmark dataset, a multispectral dataset with paired RGB and thermal images of pedestrians and cyclists. The results suggest that our method achieved an F1-score of 81.34%, indicating that our proposed approach can successfully detect pedestrians and cyclists from thermal images alone.