Comparison of Water Detection Models for an Off-road Unmanned Ground Vehicle
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Water hazards can cause unmanned ground vehicles (UGVs) to become
stuck or break down during an autonomous mission, damage electronic components
and sensors, and require costly repairs or replacements, making it crucial for UGVs to
identify water hazards in real-time, determine secure path around them, or reduce their
speed when appropriate to cross them safely. This thesis proposes a water detection
system for UGVs in off-road environment. The proposed approach combines convolutional
neural networks (CNNs) with transfer learning, leveraging their capabilities for
effective water detection. The thesis includes a comprehensive review of traditional
sensor-based methods and recent deep learning-based techniques. Real-world data collected
in off-road environments are utilized to evaluate the proposed approach, and the
method achieves a 0.50 Mean-IoU score and 92.74% accuracy on the test dataset. We
also include a comparative analysis of the method with a previous deep learning-based
semantic segmentation method for water detection. The comparison provides insights
into the relative strengths and weaknesses of these approaches for water detection in
off-road environments. Overall, this thesis provides valuable insights into the use of deep
learning for semantic segmentation in challenging environments.
Description
Keywords
Water detection, deep learning, transfer learning, object detection, convolutional neural networks (CNNs), Unmanned ground vehicles (UGVs), Off-road environments