Gravel coverage rate measurement in synchronous chip seal based on deep convolutional neural network

作     者：YIN Shi-hao DENG Ji-cai MA Yan DU Jing-yuan SHANG Xiao-kai 尹世豪;邓计才;马燕;杜靖远;尚晓凯

作者机构：School of Information Engineering Zhengzhou University 

出 版 物：《Optoelectronics Letters》 (光电子快报（英文版）)

年 卷 期：2018年第14卷第6期

页      面：447-451页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(No.61571402) 

主　　题：deep convolutional neural network synchronous chip sea Gravel coverage 

摘      要：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.