Open AccessObstacle detection for lake-deployed autonomous surface vehicles using RGB imagery
Author(s) -
Philippe Paccaud,
David Andrew Barry
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0205319
Subject(s) - obstacle , computer science , artificial intelligence , rgb color model , computer vision , pixel , feature (linguistics) , remote sensing , false positive paradox , image processing , image (mathematics) , geology , geography , linguistics , philosophy , archaeology
We describe and test an obstacle-detection system for small, lake-deployed autonomous surface vehicles (ASVs) that relies on a low-cost, consumer-grade camera and runs on a single-board computer. A key feature of lakes that must be accounted for is the frequent presence of the shoreline in images as well as the land-sky boundary. These particularities, along with variable weather conditions, result in a wide range of scene variations, including the possible presence of glint. The implemented algorithm is based on two main steps. First, possible obstacles are detected using an innovative gradient-based image processing algorithm developed especially for a camera with a low viewing angle to the water (i.e., the situation for a small ASV). Then, true and false positives are differentiated using correlation-based multi-frame analysis. The algorithm was tested extensively on a small ASV deployed in Lake Geneva. Under operational conditions, the algorithm processed 640×480-pixel images from a Raspberry Pi Camera at about 3—4 Hz on a Raspberry Pi 3 Model B computer. The present algorithm demonstrates that single-board computers can be used for effective and low-cost obstacle detection systems for ASVs operating in variable lake conditions.