Deep-Learning-Based Bughole Detection for Concrete Surface Image
Deep-Learning-Based Bughole Detection for Concrete Surface Image
复制标题
基于深度学习的混凝土表面图像漏洞检测
DOI：
10.1155/2019/8582963
复制
发表时间：
2019-06-16
影响因子：
1.8
通讯作者：
Sun, Yujia
中科院分区：
文献类型：
--
作者：
Yao, Gang;Wei, Fujia;Sun, Yujia
气孔是混凝土表面在浇注过程后出现的小坑和弹坑。传统的测量方法是通过现场人工检测进行的，检测过程耗时且难度大。提出了一种基于深度学习的混凝土表面孔洞检测方法。针对传统卷积神经网络结构中输入图像尺寸较小（28 x 28像素）和训练样本数量有限（小于10 K）的问题，通过在传统卷积神经网络结构中加入初始模块，提出了一种用于混凝土表面孔洞检测的深度卷积神经网络.考虑了真实环境中光照、阴影和几种不同表面缺陷的组合等噪声的影响。从图像测试结果来看，本文提出的DCNN具有很好的漏洞检测性能，识别准确率达到96.43%。通过与高斯拉普拉斯算法和大津方法的对比研究，该方法具有较好的鲁棒性，能够避免混凝土表面裂纹、色差和光照不均匀等因素的干扰。
Bugholes are surface imperfections that appear as small pits and craters on concrete surface after the casting process. The traditional measurement methods are carried out by in situ manual inspection, and the detection process is time-consuming and difficult. This paper proposed a deep-learning-based method to detect bugholes on concrete surface images. A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 x 28 pixels) and the limited number of labeled examples in training set (less than 10 K). The effects of noise such as illumination, shadows, and combinations of several different surface imperfections in real-world environments were considered. From the results of image test, the proposed DCNN had an excellent bughole detection performance and the recognition accuracy reached 96.43%. By the comparative study with the Laplacian of Gaussian (LoG) algorithm and the Otsu method, the proposed DCNN had good robustness which can avoid the interference of cracks, color-differences, and nonuniform illumination on the concrete surface.