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Rapid data annotation for sand-like granular instance segmentation using mask-RCNN
Abstract 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. Traditional 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 segmentation 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.
Highlights Mask-RCNN trained on individual particle data cannot segment contacting particles. A procedure of synthesizing large and diverse particle data is proposed. The trained Mask-RCNN can well segment densely packed particles. Fully autonomous testing processing for other granular objects can be established.
Rapid data annotation for sand-like granular instance segmentation using mask-RCNN
Abstract 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. Traditional 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 segmentation 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.
Highlights Mask-RCNN trained on individual particle data cannot segment contacting particles. A procedure of synthesizing large and diverse particle data is proposed. The trained Mask-RCNN can well segment densely packed particles. Fully autonomous testing processing for other granular objects can be established.
Rapid data annotation for sand-like granular instance segmentation using mask-RCNN
Zhang, Zhiyong (author) / Yin, Xiaolei (author) / Yan, Zhiyuan (author)
2021-10-04
Article (Journal)
Electronic Resource
English
| DOAJ | 2023
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| Springer Verlag | 2023