Deep Learning-Driven Culvert Monitoring: A Novel Approach for Flash Flood Mitigation through Visual Blockage Analysis DOI
Betty Elezebeth Samuel, Sultan Alghamdi,

T. K. S. Lakshmi Priya

и другие.

2022 IEEE 7th International conference for Convergence in Technology (I2CT), Год журнала: 2024, Номер unknown

Опубликована: Апрель 5, 2024

The primary cause of urban flash floods is often cited as trash clogging culverts. Flash can be avoided with the help intelligent video analytic (IVA) methods that extract information about blockages in order to make maintenance-related decisions a timely manner. Knowing percentage culverts are visually blocked prioritise maintenance at heavily locations. In this paper, we introduce deep learning-powered segmentation-classification pipeline for automatically detecting and segmenting culvert openings then classifying them into one four categories based on degree which they blocked. learning models underwent training using datasets sourced from Visual Hydraulics Blockage Dataset (VHD) Images Culverts (ICOB). outcomes revealed Mask R-CNN attains highest performance segmentation, achieving an mAP@75 score 77.2%. contrast, NASNet excelled classification tasks, remarkable 81.2% accuracy test data. To underscore practical importance these findings, potential application demonstrated, involves monitoring visual obstructions.

Язык: Английский

Artificial Intelligence of Things (AIoT)-oriented framework for blockage assessment at cross-drainage hydraulic structures DOI Creative Commons
Umair Iqbal, Muhammad Zain Bin Riaz, Johan Barthélemy

и другие.

Australasian Journal of Water Resources, Год журнала: 2023, Номер unknown, С. 1 - 11

Опубликована: Дек. 19, 2023

Язык: Английский

Процитировано

4
Deep Learning-Driven Culvert Monitoring: A Novel Approach for Flash Flood Mitigation through Visual Blockage Analysis DOI
Betty Elezebeth Samuel, Sultan Alghamdi,

T. K. S. Lakshmi Priya

и другие.

2022 IEEE 7th International conference for Convergence in Technology (I2CT), Год журнала: 2024, Номер unknown

Опубликована: Апрель 5, 2024

The primary cause of urban flash floods is often cited as trash clogging culverts. Flash can be avoided with the help intelligent video analytic (IVA) methods that extract information about blockages in order to make maintenance-related decisions a timely manner. Knowing percentage culverts are visually blocked prioritise maintenance at heavily locations. In this paper, we introduce deep learning-powered segmentation-classification pipeline for automatically detecting and segmenting culvert openings then classifying them into one four categories based on degree which they blocked. learning models underwent training using datasets sourced from Visual Hydraulics Blockage Dataset (VHD) Images Culverts (ICOB). outcomes revealed Mask R-CNN attains highest performance segmentation, achieving an mAP@75 score 77.2%. contrast, NASNet excelled classification tasks, remarkable 81.2% accuracy test data. To underscore practical importance these findings, potential application demonstrated, involves monitoring visual obstructions.

Язык: Английский

Процитировано

0