Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset

Sensors

Jun Heo¹  Changsun Ahn²  Wansik Choi² 

[1] Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA;School of Mechanical Engineering, Pusan National University, Busan 46241, Korea;

关键词: deep neural network;    CycleGAN;    road surface detection;    road friction detection;   

DOI  :  10.3390/s21227769

来源: DOAJ

【 摘 要 】

Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy.

【 授权许可】

Unknown