Automatic road detection system for an air–land amphibious car drone

• Published in: Future generation computer systems 2018-08, Vol.85, p.51-59
• Main Authors: Li, Yujie, Lu, Huimin, Nakayama, Yoshiki, Kim, Hyoungseop, Serikawa, Seiichi
• Format: Article
• Language: English
• Subjects: Air–land amphibious car drone; Road detection; Unmanned aerial vehicle
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2018.02.036

In recent years, unmanned aerial vehicle (UAV) technologies have rapidly developed. Drones, which are one type of UAV, are used in many industrial fields, such as photography, delivery and agriculture. However, a commercial drone can fly for only approximately 20 min on one charge. Furthermore, drones are prohibited from flying in some areas, and cannot be operated in bad weather. Due to the development of drone technologies, we must reduce energy consumption and achieve long-range movement. To overcome these limitations, we develop a new air–land amphibious car drone that can fly and requires less power consumption in land mode; this extends the range of mobility of the drone. Moreover, land mode can be used to pass through restricted areas or bad weather conditions by sliding. Furthermore, we develop a Convolutional Neural Network (CNN)-based algorithm for detecting the road in a captured scene. To more accurately segment the road region based on images from the equipped camera of the drone, we propose atrous spatial pyramid pooling (ASPP) ResNet blocks, instead of Resblocks, which were proposed by DeepLab. The experimental results demonstrate that the proposed method improves the pixel accuracy (PA) to 85.6% and achieves a mean Intersection over Union (mIoU) of 55.8%. •A new air–land amphibious car drone that can fly and requires less power consumption in land mode is proposed.•We develop a Convolutional Neural Network (CNN)-based algorithm for detecting the road in a captured scene.•A car drone for operating in extreme environments is proposed.