Classification of Coarse Aggregate Particle Size Based on Deep Residual Network

Abstract

Traditional aggregate particle size detection mainly relies on manual batch sieving, which is time-consuming and inefficiency. To achieve rapid automatic detection of aggregate particle sizes, a mechanical symmetric classification model of coarse aggregate particle size, based on a deep residual network, is proposed in this paper. First, aggregate images are collected by the optical vertical projection acquisition platform. The collected aggregate images are corrected, and their geometric parameters are extracted. Second, various digital image processing methods, such as size correction and morphological processing, are used to improve the image quality and enlarge the image dataset of different aggregate particle sizes. Then, the deep residual network model (ResNet50) is built to train the aggregate image dataset to achieve accurate classification of aggregate sizes. Finally, compared with the traditional single geometric particle size classification model, the comparative results show that the accuracy of the coarse aggregate classification model proposed in this paper is nearly 20% higher than that of the traditional method, reaching 0.833. The proposed model realizes the automatic classification of coarse aggregate particle size, which can significantly improve the efficiency of aggregate automatic detection.