Automatic Stereo Vision-Based Inspection System for Particle Shape Analysis of Coarse Aggregates

Abstract

Particle shape analysis of coarse aggregates is important to ensure the quality of cement and asphalt concrete mixtures. Conventional methods for measuring the aggregate particle size, such as manual calipers or mechanical sieving, are time consuming and labor intensive. In addition, the accuracy of image processing techniques is severely limited by shadows and heterogeneous backgrounds. Hence, we developed an automatic stereo vision-based inspection system (SVIS) for the identification and shape analysis of coarse aggregate particles. We integrated a cascaded deep learning model into the SVIS to identify the types of coarse aggregate particles under offsite working conditions. Moreover, we combined deep learning and stereo vision techniques to calculate the unit conversion factors and the thickness of each particle to facilitate particle shape analysis. The precision and recall metrics obtained from the training model were ≥96.0% for particle detection and ≥95.7% for particle segmentation. In the experiment, the proposed inspection system accurately determined the particle size of coarse aggregates with measurement errors of ≤4.96% compared with the ground truth. Thus, the proposed system overcomes the shortcomings of image processing technologies and considerably aids the decision-making process during onsite material inspection.