Title

Semantic Segmentation of Flooding for Flooding Detection

Description/Abstract/Artist Statement

Among all natural disasters, flooding occurs frequently around the world, causing significant harm to humans, livestock and property. The overall goal for this project is to establish a real-time flood detection system that can warn motorists of flooding in relevant areas. Semantic segmentation of water is naturally required to build such a flood detection system. In this work, we present a semantic segmentation model trained to segment water in images and videos of flooding. We first train it on a unique combination of all preexisting semantic segmentation datasets with water and perform manipulations and mappings to these datasets to prepare them for training. Afterwards, we perform a grid search to find the most optimal neural network and corresponding loss function for the prepared dataset. The result is a neural network that can efficiently and accurately detect water in images and videos of flooding. Our model converged to a binary accuracy of 87%, mIOU of 56%, F1 score of 67%, F2 score of 70%, precision of 70%, and recall of 76% on our prepared dataset. These statistics translate well into reality as indicated by the video linked here with blue representing flooding and red representing not flooding: https://www.youtube.com/watch?v=UIAW1r1Zqco&ab_channel=StephenLamczyk

Presenting Author Name/s

Stephen Lamczyk

Faculty Advisor/Mentor

Khan Iftekharuddin

College Affiliation

College of Engineering & Technology (Batten)

Presentation Type

Oral Presentation

Disciplines

Artificial Intelligence and Robotics | Vision Science

Session Title

Interdisciplinary Research #1

Location

Zoom

Start Date

3-19-2022 2:15 PM

End Date

3-19-2022 3:15 PM

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Mar 19th, 2:15 PM Mar 19th, 3:15 PM

Semantic Segmentation of Flooding for Flooding Detection

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Among all natural disasters, flooding occurs frequently around the world, causing significant harm to humans, livestock and property. The overall goal for this project is to establish a real-time flood detection system that can warn motorists of flooding in relevant areas. Semantic segmentation of water is naturally required to build such a flood detection system. In this work, we present a semantic segmentation model trained to segment water in images and videos of flooding. We first train it on a unique combination of all preexisting semantic segmentation datasets with water and perform manipulations and mappings to these datasets to prepare them for training. Afterwards, we perform a grid search to find the most optimal neural network and corresponding loss function for the prepared dataset. The result is a neural network that can efficiently and accurately detect water in images and videos of flooding. Our model converged to a binary accuracy of 87%, mIOU of 56%, F1 score of 67%, F2 score of 70%, precision of 70%, and recall of 76% on our prepared dataset. These statistics translate well into reality as indicated by the video linked here with blue representing flooding and red representing not flooding: https://www.youtube.com/watch?v=UIAW1r1Zqco&ab_channel=StephenLamczyk