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.