Integration of ROV and vision-based underwater inspection for Limnoperna fortunei in water conveyance structure

The invasion of Limnoperna fortunei (L. fortunei) has been identified as one major biofouling in the operation of hydraulic engineering, which not only corrodes the concrete structures but also reduces the pipe diameter and increases the surface roughness, leading to the decrease of water conveyance capacity and the increase of the project operation cost. To better cope with this problem, an automated underwater inspection analysis scheme for the biofouling of L. fortune is provided in this study, which innovatively integrates the underwater remote operating rover (ROV) and computer vision techniques to inspect and evaluate the invasion of L. fortunei in water conveyance structure. This scheme first presents an image enhancement approach based on the fusion strategy to improve the quality of images extracted from underwater robot inspection videos. Then, the L. fortunei is segmented by U-Net in the enhanced underwater images, and the definition of adherent area ratio quantitatively assesses the biofouling severity. At last, the underwater inspection analysis scheme is implemented in a typical aqueduct, and the automatic analysis results are compared with the field investigation during the emptying maintenance of the aqueduct. In this study, the dataset of real ROV inspection video sequences was first used to evaluate the effectiveness of the proposed method for inspecting L. fortunei invasions in realistic scenarios, and then for the comparison with state-of-the-art methods. The results show that the proposed automated inspection scheme is capable of efficiently improving the underwater imaging quality and accurately detecting the L. fortunei.