中国农村水利水电Issue(3)：57-63,7.DOI:10.12396/znsd.2500650
基于改进YOLOv8级联架构的水工金属结构锈蚀检测与等级判定方法
Corrosion Detection and Grade Determination Method for Hydraulic Metal Structures Based on I mproved YOLOv8 Cascade Architecture
摘要
Abstract
To mitigate the subjectivity of rust annotation and enhance annotation efficiency,this paper proposes a rust detection method based on a YOLOv8 cascaded architecture to rapidly implement deep learning-based corrosion detection for hydraulic engineering metal structures.To enhance accurate detection of rust target,the MobileViTv3 module is integrated into YOLOv8n,resulting in a modified model named YOLOv8-vit.Based on YOLOv8-vit,we further propose YOLOv8-vit-cls for rust grade learning and classification.This network leverages the pretrained parameters of YOLOv8-vit to efficiently learn features of different rust grades.Finally,a cascaded architecture combining YOLOv8-vit and YOLOv8-vit-cls is constructed to perform both rust detection and grade classification of hydraulic metal structures.关键词
水工金属结构/锈蚀检测/锈蚀等级/深度学习Key words
hydraulic metal structures/rust detection/rust grade/deep learning分类
建筑与水利引用本文复制引用
朱思思,胡兴,程浩东,康飞..基于改进YOLOv8级联架构的水工金属结构锈蚀检测与等级判定方法[J].中国农村水利水电,2026,(3):57-63,7.基金项目
中国长江电力股份有限公司科研项目资助(合同编号:Z232402014). （合同编号:Z232402014）
