Image-Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam

Yonglong Li,Jialong Li,Shuang Wang,Hua Zhang,Haoran Wang

doi:10.1155/2019/6924976

Abstract

The abrasion of stilling basin slabs which is caused by waterborne particles is one of the main surface damages in the operation of hydropower station. For determining whether to repair the stilling basin slabs, periodic inspections of erosion condition of stilling basin slabs are required. The practical problem is how to get the underwater image without unwatering and how to analyse the abrasion though the images. This paper developed a novel underwater inspection system named UIS-1 which consists of a customized underwater robot and special quantitative analysis method for this situation. Firstly, the integrated component was designed for the underwater robot that partially removes the siltation and obtains the image of the concrete surface of stilling basin slabs in the desired position. Secondly, the paper proposed an image algorithm to obtain aggregate exposure ratio for quantitative abrasion analysis. This image algorithm used SLIC superpixel and the SVM machine learning method to detect the coarse aggregate exposure automatically. Then, the aggregate exposure ratio was calculated to analyse the degree of abrasion. Finally, the UIS-1 system was evaluated in the field experiments of a dam in Sichuan, China, and its performance was discussed by comparison.

Highlights

Stilling basin is one of the most commonly used structures for the energy dissipation in dams. e purpose of this structure is to minimize the scouring effects which can occur downstream of the flow
Not like other hydraulic structures, abrasion erosion which is caused by friction and impact of waterborne debris on the concrete surface is a major damage of stilling basin slabs [1]. ere are some traditional underwater methods used for dam erosion detection such as sonar [2, 3] and ground-penetrating radar [4]
To overcome the limitations of diver inspection, many underwater robots have been developed for dam inspection. e article [5] proposed an autonomous underwater vehicle (AUV) for automatically surveying a dam’s wall while snapping pictures and gathering navigation data in order to build a globally optimized and georeferenced photomosaic to enable systematic inspections

Summary

Introduction

Stilling basin is one of the most commonly used structures for the energy dissipation in dams. e purpose of this structure is to minimize the scouring effects which can occur downstream of the flow. E article [5] proposed an autonomous underwater vehicle (AUV) for automatically surveying a dam’s wall while snapping pictures and gathering navigation data in order to build a globally optimized and georeferenced photomosaic to enable systematic inspections Another AUV system [6] was developed to inspect hydroelectrics which includes vision system to detect and measure cracks. With the development of image-processing techniques, computer vision-based methods have been widely studied in the damages on the surfaces of concrete structures including cracks, spalling, efflorescence, and holes [16]. To the best of authors’ knowledge, there is no research on the abrasion quantitative analysis using automatic aggregate detection from the underwater stilling basin slabs images. Due to insufficient sample of the images of stilling basin slabs, the above deep learning-based algorithms cannot apply to aggregate detection. (2) We proposed image algorithm used superpixel and the SVM machine learning method to detect the coarse aggregate automatically. en, aggregate exposure ratio can be calculated to quantitatively analyse the damage of the abrasion

System Design

Underwater Inspection Robot Design

Findings

Result of detection