Corrosion Detection and Grading Method for Hydraulic Metal Structures Based on an Improved YOLOv10 Sequential Architecture
Haodong Cheng, Fei Kang
Abstract
Herein, we present a method for detecting and determining the corrosion level of hydraulic metal structure surfaces through images while reducing the difficulty of dataset annotation. To achieve accurate detection of corrosion targets, the MobileViTv3 block is integrated into YOLOv10, resulting in the proposed YOLOv10-vit for corrosion target detection. Based on YOLOv10-vit, the YOLOv10-vit-cls classification network is introduced for corrosion level determination. This network leverages the pre-trained parameters of YOLOv10-vit to more quickly learn the features of different corrosion levels. To avoid subjective factors in the corrosion level annotation process and reduce annotation difficulty, a cascaded corrosion detection architecture combining YOLOv10-vit and YOLOv10-vit-cls is proposed. Finally, based on the proposed corrosion detection architecture, we achieve accurate corrosion detection and level determination for hydraulic metal structures.