Vision-Based Real-Time Obstacle Segmentation Algorithm for Autonomous Surface Vehicle

• Published in: IEEE access Vol. 7; pp. 179420 - 179428
• Main Authors: Kim, Hanguen, Koo, Jungmo, Kim, Donghoon, Park, Byeolteo, Jo, Yonggil, Myung, Hyun, Lee, Donghwa
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous surface vehicle; Barriers; Computational modeling; Computer architecture; computer vision; deep learning; Embedded systems; Frames per second; Image segmentation; Marine environment; Object recognition; obstacle segmentation; Real time; Real-time systems; Sea surface; Segmentation; Sensors; ship navigation; Surface vehicles; Vision; Vision sensors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2959312

Among various sensors used to recognize obstacles in marine environments, vision sensors are the most basic. Vision sensors are significantly affected by the surrounding environment and cannot recognize distant objects. However, despite these drawbacks, they can detect objects that radars cannot detect in nearby regions. They can also recognize small obstacles such as boats that are not equipped with an automatic identification system (AIS) or buoys. Thus, vision sensors and radar can be used in a complementary manner. This paper proposes a vision sensor-based model, called Skip-ENet, for recognizing obstacles in real time. Compared with ENet, the amount of computation is not significantly higher. Further, Skip-ENet can segment complex marine obstacles effectively by increasing the values for the class accuracy and mean Intersection of Union (mIoU). Moreover, this model enables even low-cost embedded systems to compute 10 or more frames per second (fps). The superiority of the proposed model was verified by comparing its performance with that of the conventional segmentation models, MobileNet, ENet, and DeeplabV3+.