A detection method of elongated and flat aggregate particles based on multi-view shape features with a single camera

Abstract Aggregate is an important component of asphalt mixtures, and its shape has a significant influence on the road quality. In this study, a single industrial camera was used to collect images of aggregate particles during falling; then, their morphologies obtained in multiple views were analyzed. Using the equivalent geometric model, four shape characterization parameters—area variety factor, minor diameter variety factor, maximum elongation factor, and Strip-Block area variety factor—were proposed to compose the multi-view shape feature. On this basis, a general regression neural network was adopted to realize the classification of aggregate particles. The results show that the aggregate classification is slightly different when using different equivalent geometric models, while the aggregate shape can be effectively classified. The accuracy of aggregate classification can be improved by fusing parameters from different equivalent models using principal component analysis; another way is through increasing the frame rate of image collection that may increase the number of views. In general, the findings indicate that the proposed detection method can be applied to actual road engineering, which is of great significance to guarantee pavement quality.