Automatic segmentation of concrete aggregate using convolutional neural network

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The segmentation of concrete aggregates in sedimentation images plays a significant role in evaluating the particle distribution and stability properties. To address the limitations of traditional evaluation methodologies that are subjective and labor-intensive, a deep-learning-based concrete aggregate segmentation method is proposed. In the proposed approach, an encoder–decoder structure was adopted. Specifically, our model adds a squeeze-and-excitation block to ResNeXt50 to adaptively recalibrate the channel-wise feature response and increase the efficiency of feature extraction. A decoder module was adopted to refine the segmentation results. The combined loss function was applied to overcome the problem of sample imbalance while improving model performance. The trained model achieves accurate segmentation of the aggregate and suspension on the images in the test set, and its performance is superior to that of the other three popular segmentation methods, achieving 97.85% precision, 95.81% recall, 96.81% F1 score, and 93.85% intersection over union.