Work

Semantic Segmentation of Ship Hulls for Underwater Cleaning Robots

Year: 2025

Type: article

Abstract: Underwater barnacle cleaning robots require a semantic segmentation algorithm deployable onboard to effectively segment barnacles, obstacles, and background on ship hulls. This study independently constructed a dedicated ship hull semantic segmentation dataset and implemented an improved BiSeNet network. The approach integrates features from both a spatial path and a context path. Building upon the cross-entropy loss function, class weights were strategically assigned during training according to the distribution of different target categories within the dataset. This method achieved significantly high Intersection over Union (IOU) and F1 scores.

Source: Journal of Computer Science and Electrical Engineering

DOI: 10.61784/jcsee3087

Authors: Lu Li, Wei Fang, YiGe Shang, Zhi Zhang, MingFu Li, DaoYi Chen

Institutions: Tsinghua Shenzhen International Graduate School, Tsinghua University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7082629485

FWCI: 0.362

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Cited by: 1

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