YIN Shi-hao,DENG Ji-cai,MA Yan,DU Jing-yuan.Gravel coverage rate measurement in synchronous chip seal based on deep convolutional neural network[J].Optoelectronics Letters,2018,14(6):447-451 Gravel coverage rate measurement in synchronous chip seal based on deep convolutional neural network Author Name Affiliation YIN Shi-hao  School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China  DENG Ji-cai  School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China  MA Yan  School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China  DU Jing-yuan  School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China  Abstract:       Synchronous chip seal is an advanced road constructing technology, and the gravel coverage rate is an important indicator of the construction quality. In this paper, a novel approach for gravel coverage rate measurement is proposed based on deep learning. Convolutional neural network (CNN) is used to segment the image of ground covered with gravels, and the gravel coverage rate is computed by the percentage of gravel pixels in the segmented image. The gravel coverage rate dataset for model training and testing is built. The performance of fully convolutional neural network (FCN) and U-Net model in the dataset is tested. A better model named GravelNet is constructed based on U-Net. The scaled exponential linear unit (SELU) is employed in the GravelNet to replace the popular combination of rectified linear unit (ReLU) and batch normalization (BN). Data augmentation and alpha dropout are performed to reduce overfitting. The experimental results demonstrate the effectiveness and accuracy of our proposed method. Our trained GravelNet achieves the mean gravel coverage rate error of 0.35% on test dataset. Hits: 1072 Download times: 0 View Full Text    Download reader