Geodesic
A Comparison of Deep Learning Algorithms on Image Data for Detecting Floodwater on Roadways
Computer Science and Information Systems, Tome 19 (2022) no. 1.

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Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show that the proposed Mask-R-CNN-based floodwater detection and segmentation outperform previous studies, whereas the GAN-based model has a straightforward implementation compared to other models.
Keywords: Floodwater detection; Mask-R-CNN; GAN; object detection and segmentation 
@article{CSIS_2022_19_1_a19,
     author = {Salih Sarp and Murat Kuzlu and Yanxiao Zhao and Mecit Cetin and Ozgur Guler},
     title = {A {Comparison} of {Deep} {Learning} {Algorithms} on {Image} {Data} for {Detecting} {Floodwater} on {Roadways}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {19},
     number = {1},
     year = {2022},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2022_19_1_a19/}
}
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Salih Sarp; Murat Kuzlu; Yanxiao Zhao; Mecit Cetin; Ozgur Guler. A Comparison of Deep Learning Algorithms on Image Data for Detecting Floodwater on Roadways. Computer Science and Information Systems, Tome 19 (2022) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2022_19_1_a19/