Year: 2025
Type: article
Abstract: Water surface inspection is a crucial instrument for safeguarding the aquatic environment. UAVs enhance the efficiency of water area inspections due to their high mobility and extensive coverage. This paper introduces two UAV inspection methodologies for the characteristics of rivers and lakes, along with an efficient semantic segmentation algorithm, WaterSegLite (Water Segmentation Lightweight algorithm), for UAV visual navigation. The algorithm employs the UAV's aerial perspective alongside a streamlined neural network architecture to facilitate rapid real-time segmentation of water bodies and to furnish positional data to the UAV for visual navigation. The experimental findings indicate that WaterSegLite achieves a segmentation accuracy (mIoU) of 93.81% and an F1 score of 95.44%, surpassing the baseline model by 2.7% and 2.23%, respectively. Simultaneously, the processing frame rate of this algorithm on the airborne device attains 28.27 frames per second, fully satisfying the requirements for real-time water surface inspection by UAVs. This paper offers technical assistance for UAV inspection techniques in aquatic environments and presents innovative concepts for the intelligent advancement of water surface inspection.
Source: Sensors
DOI: 10.3390/s25082600
Authors: Jianfeng Han, Xiongwei Gao, Lili Song, Jiandong Fang, Yongzhao Tao, Haixin Deng, Jie Yao
Institution: Inner Mongolia University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G3746667057, G5917804903
OpenAlex ID: W4409606560