Abstract. Water bodies, such as puddles and flooded areas, on and off
road pose significant risks to autonomous cars. Detecting water from
moving camera is a challenging task as water surface is highly refractive,
and its appearance varies with viewing angle, surrounding scene, weather
conditions. In this paper, we present a water puddle detection method
based on a Fully Convolutional Network (FCN) with our newly proposed
Reflection Attention Units (RAUs). An RAU is a deep network unit designed to embody the physics of reflection on water surface from sky and
nearby scene. To verify the performance of our proposed method, we collect 11455 color stereo images with polarizers, and 985 of left images are
annotated and divided into 2 datasets: On Road (ONR) dataset and Off
Road (OFR) dataset. We show that FCN-8s with RAUs improves significantly precision and recall metrics as compared to FCN-8s, DeepLab
V2 and Gaussian Mixture Model (GMM). We also show that focal loss
function can improve the performance of FCN-8s network due to the
extreme imbalance of water versus ground classification problem