Aggregate boundary recognition of asphalt mixture CT images based on convolutional neural networks

Abstract

This study aims to propose an intelligent aggregate boundary segmentation algorithm based on convolutional neural networks (CNNs) and watershed algorithm for quickly recognising the boundary of aggregates on asphalt mixture CT images. CNN was concisely introduced. An aggregate boundary segmentation method for asphalt mixture CT images based on CNN and watershed algorithm was depicted in detail. The generalisation ability, that is, the effectiveness of image segmentation method by CNN and watershed algorithm was also evaluated. Results showed that the intelligent segmentation algorithm proposed by combining CNNs and watershed algorithm could effectively segment the aggregate boundaries on asphalt mixture CT images with different levels of boundary definition. The adhesion between aggregates on asphalt mixture CT images could be reduced using a custom multi-threshold segmentation (CMTS) method. The intelligent image segmentation algorithm had more accurate segmentation and more convenient operation than Canny and multi-threshold algorithms.

KEYWORDS:

• Asphalt mixture CT image
• aggregate boundary segmentation
• convolutional neural network
• watershed algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All models and data that support the findings of this study, such as AMCT-NET-W-based model and test output data, are available from the corresponding author upon reasonable request.