게재연도 | 2021년 |
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논문집명 | Road Materials and Pavement Design |
논문명 | Counting of exposed aggregate number on pavement surface based on computer vision technique |
저자 | Lyhour Chhay, Lae-Jeong Park, Young Kyu Kim, Seung Woo Lee |
구분 | 국외저널 |
요약 | Functional performance of exposed aggregate concrete pavement (EACP), such as tyre-pavement noise, is influenced by the surface texture depth and wavelength. To minimise tyre-pavement noise, the texture wavelength, which is represented by the exposed aggregate number (EAN), should be controlled. Normally, the EAN is conducted by manual counting. It requires much human efforts. Therefore, this study suggests an efficient method to count the EAN on a digital image of EACP surface through the deep learning model faster region-based convolutional neural network (Faster R-CNN). The TensorFlow Object Detection API was used to adjust the parameters in the training model. Results of the suggested model were compared with the manual counting in the mock-up and field test dataset. The result showed that the mean absolute error was 5.34 and 8.19 for the mock-up and field tests, respectively. Therefore, the proposed method can be used to preliminarily estimate the EAN under specified condition. |
핵심어 | Exposed aggregate concrete pavement, exposed aggregate counting, object detection and counting, computer vision, TensorFlow object detection API |