Convolutional Neural Networks for Road Detection: An Unsupervised Domain Adaptation Approach

Abstract

Due to the frequent road network changes, keeping them updated is fundamental for several purposes. Currently, models based on Deep Learning (DL), specifically, Convolutional Neural Networks (CNNs), such as encoder-decoder type, are state-of-the-art for this purpose. In this context, the high performance in CNNs has two aspects involved: the model needs a large labeled dataset, and the dataset belongs to the same probability distribution. In practical applications, however, this may not hold, since there is a domain shift effect, and it is not customary for the availability of labeled data. To approach these challenges, we propose to adapt the U-Net architecture (encoder-decoder) to the Unsupervised Domain Adaptation (UDA) that does not need labeling data to minimize the domain shift effect. Our results demonstrate that the proposed method contributes to road segmentation, whose model reaches 74.31% (IoU) and 85.04% (F1), against the same model without UDA that reaches 67.36% (IoU) and 80.02% (F1). This implies that the information that comes from the target domain, even unsupervised, contributes to adversarial learning, improving the generalization capacity of the model, enhancing aspects such as better discrimination surrounding classes, and in the geometric delineation of the road network.