An automated machine-vision method for coarse aggregate gradation measurement using attention-based instance segmentation

Aggregate gradation is a key indicator influencing the mechanical performance of concrete structures. To enhance the efficiency of concrete aggregate gradation detection, this study proposes an artificial intelligence-based automated coarse aggregate gradation detection method based on machine vision, incorporating attention mechanisms and feature-path fusion to improve traditional segmentation networks. Additionally, it introduces the Guldinus theorem for the rapid computation of particle morphological characteristics. The proposed method consists of three main components: image pixel correction, a vision-based coarse aggregate instance segmentation neural network, and a gradation curve calculation approach derived from the Guldinus theorem. Through field verification and comparison with traditional sieving methods, the proposed automated approach achieved an average absolute deviation of less than 1% and a root-mean-square error of less than 2%, significantly outperforming the engineering tolerance limit of ±5%, while enabling batch-level detection within seconds. The improved mask region-based convolutional neural network, which integrates the shifted-window transformer and path aggregation feature pyramid network, demonstrates superior segmentation accuracy compared with the conventional mask region-based convolutional neural network, particularly for small particle sizes. Furthermore, the Guldinus-theorem-based gradation estimation method enables accurate and reliable computation of aggregate gradation. The findings of this study provide a robust and efficient artificial intelligence application framework for automated coarse aggregate gradation analysis in engineering practice.