Detecting Floodwater on Roadways from Image Data Using Mask-R-CNN

Sarp, Salih; Kuzlu, Murat; Cetin, Mecit; Sazara, Cem; Güler, Özgür

doi:10.1109/inista49547.2020.9194655

Cited by 22 publications

(18 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method has advantages when calculating pixel-level contours, such as the boundaries of water bodies and bridges. Previous research has shown that using the segmentation network to detect floods and calculate the flood depth is a practical approach [ 65 , 66 , 67 ].…”

Section: Field Deployment and Resultsmentioning

confidence: 99%

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

Lin

Lee

Chang

et al. 2021

Sensors

View full text Add to dashboard Cite

Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bridge-safety capacity need to be monitored during flood periods to protect the traveling public. In the present study, a scour monitoring system designed with vibration-based arrayed sensors consisting of a combination of Internet of Things (IoT) and artificial intelligence (AI) is developed and implemented to obtain real-time scour depth measurements. These vibration-based micro-electro-mechanical systems (MEMS) sensors are packaged in a waterproof stainless steel ball within a rebar cage to resist a harsh environment in floods. The floodwater-level changes around the bridge pier are performed using real-time CCTV images by the Mask R-CNN deep learning model. The scour-depth evolution is simulated using the hydrodynamic model with the selected local scour formulas and the sediment transport equation. The laboratory and field measurement results demonstrated the success of the early warning system for monitoring the real-time bridge scour-depth evolution.

show abstract

Section: Field Deployment and Resultsmentioning

confidence: 99%

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

Lin

Lee

Chang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Table 1 compares a recent study [39] with the proposed Mask-R-CNN and GAN models. Our Mask-R-CNN model has better precision, recall, and F1-score results over the Fully Convolutional Neural Network (FCN) based on a pre-trained VGG-16 network [2].…”

Section: Comparison Of the Modelsmentioning

confidence: 99%

A comparison of deep learning algorithms on image data for detecting floodwater on roadways

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Pose estimation is the detection of body parts in images or videos. Most studies [9][10][11][12][13][14][15][16] use a top-down approach for detection and estimation. As the first step in this approach, the person's part is detected, and then the body parts are estimated.…”

Section: Related Workmentioning

confidence: 99%

Realization of a Real-Time Decision Support System to Reduce the Risk of Diseases Caused by Posture Disorders Among Computer Users

Gumuskaynak¹,

Toptas²,

Aslantas³

et al. 2022

Digital Interaction and Machine Intelligence

Self Cite

View full text Add to dashboard Cite

Nowadays, diseases caused by posture disorders are becoming more common. This situation reduces the working efficiency of people, especially computer users. This study aims to provide prevention of diseases caused by posture disorders faced by computer users and realize an application software to reduce disease risks. With this realized application, computer users’ movements are monitored through the camera, and the situations that may pose a risk of disease for the users are determined. Realized application software is a decision support system. This decision support system provides users suggestions to change their position according to their instant postures and supports them to work more efficiently. The user data is collected by processing the images taken from a camera using the developed computer vision algorithm. Two-dimensional (2D) human exposure estimation is performed with the obtained data. The situations that can decrease the working efficiency are specified with the data obtained from exposure estimation using the developed model. As a result of these findings, increasing the working efficiency is provided by informing the user in real-time about the situation that may decrease the working efficiency.

show abstract

Detecting Floodwater on Roadways from Image Data Using Mask-R-CNN

Cited by 22 publications

References 9 publications

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

A comparison of deep learning algorithms on image data for detecting floodwater on roadways

Realization of a Real-Time Decision Support System to Reduce the Risk of Diseases Caused by Posture Disorders Among Computer Users

Contact Info

Product

Resources

About