Weakly supervised deep learning-based concrete aggregates automatic segmentation for assessing separation degree

Abstract

Vision-based automatic concrete aggregate segmentation helps to facilitate fast quantitative analysis of concrete aggregate shape, gradation, and uniformity. However, existing methods are mainly based on supervised deep learning. Since concrete surface images often contain numerous aggregates with irregular sizes and boundaries, this leads to a time-consuming and labor-intensive data annotation process. To reduce annotation costs, this study proposes a weakly supervised deep learning (WSDL) concrete aggregates segmentation method, which can train accurate pixel-level concrete aggregates segmentation models using only low-cost bounding box-level annotation. A concrete aggregate segmentation dataset containing 1400 images is created and experiments are conducted. The results show that the proposed method can achieve pixel-level concrete aggregate segmentation with an AP50 of 95.27 %. Compared with the supervised deep learning-based method, the annotation cost is reduced by 86.24 %. This study also verifies the feasibility of the proposed method for quantitative evaluation of concrete aggregate segregation degree. Expert systems for automatic analysis of concrete aggregate gradation and uniformity can be developed based on this method in the future.