Rapid Data Annotation For Sand-Like Granular Instance Segmentation Using Mask-Rcnn

Image processing, as an efficient and accurate technology, has been widely applied to characterize granular object morphology in many fields, such as construction engineering, material science, agriculture, etc. Tradi-tional static image processing is not autonomous because it cannot automatically segment contacting particles. In contrast, the current deep-learning-based algorithms can achieve high degree of autonomy in instance seg-mentation given it is well trained. However, lack of training data is a common pain point as it requires extensive manual labour using the conventional labelling tools. In this study, using sand as an example, we proposed a mask labelling methodology that can establish a large and diverse training set without manual labelling. The trained Mask-RCNN demonstrates excellent performance on a densely packed particle image. Using the data labelling method proposed in this study and the deep-learning algorithms, fully automated image processing can be realized for granular materials without massive manual labelling workload.