水力发电学报2026，Vol.45Issue(9)：117-130,14.DOI:10.11660/slfdxb.20260911
面向水利工程图像管理的目标检测驱动分类归档方法
Object detection-driven archiving method for hydraulic engineering image management
摘要
Abstract
In contemporary hydraulic engineering management,daily inspections and specialized surveys generate numerous on-site images.However,the images captured by multiple teams are often uploaded without classification,leading to information overload and ineffective use of the historical images for data analysis.To overcome the low efficiency of manual classification,this paper describes an intelligent archiving framework based on an improved YOLOv8 detector,enabling automated processing from object detection to image classification.We construct a dedicated dataset equipped with 12 types of typical hydraulic engineering targets,integrate the EMA attention mechanism to enhance multi-scale feature representation,and adopt the SIoU loss function to raise the bounding box regression accuracy.Experimental results show this new archiving model achieves mAP50-95 of 0.624 and mAP50 of 0.793,or an improvements of 9.00%and 8.83%over the baseline model,respectively.It leverages the structured information-such as target categories,positions,and scales,derived from detection outputs-and therefore enables the multi-dimensional automatic archiving of images according to the engineering components and work scenarios,achieving an accuracy of 95.4%on a test set comprising 3,348 images.The detection-driven archiving(DDA)method demonstrates an effective approach to the intelligent management of hydraulic engineering image datasets.关键词
水利工程管理/目标检测/机器视觉/YOLOv8/智能归档/注意力机制/损失函数Key words
hydraulic engineering images/object detection/machine vision/YOLOv8/intelligent archiving/attention mechanism/loss function分类
建筑与水利引用本文复制引用
陈创威,刘建文..面向水利工程图像管理的目标检测驱动分类归档方法[J].水力发电学报,2026,45(9):117-130,14.基金项目
广东省自然科学基金-面上项目(2025A1515011569) （2025A1515011569）
广东省水利科技创新项目(2024-07) （2024-07）
