A Comparison of Deep Learning Algorithms on Image Data for Detecting Floodwater on Roadways

Sarp S., Kuzlu M., Zhao Y., Cetin M., Guler O.

Computer Science and Information Systems, cilt.19, sa.1, ss.397-414, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.2298/csis210313058s
  • Dergi Adı: Computer Science and Information Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.397-414
  • Anahtar Kelimeler: Floodwater detection, GAN, Mask-R-CNN, Object detection and segmentation
  • Yozgat Bozok Üniversitesi Adresli: Hayır

Özet

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.