![]() ![]() The two sub-networks are trained in an end-to-end to jointly optimize parameters and produce the final output of localizing important cracks. Specifically, the two sub-networks are built on convolution-deconvolution CNN architectures, but also they are comprised of different functional components to achieve their own goals efficiently. The CCA network is to learn gradient component regarding cracks, and the CRA network is to learn a region-of-interest by distinguishing critical cracks and noise such as scratches. ![]() To this aim, the proposed algorithm uses a two-branched CNN architecture, consisting of sub-networks named a crack-component-aware (CCA) network and a crack-region-aware (CRA) network. In this paper, we propose an autonomous crack detection algorithm based on convolutional neural network (CNN) to solve the problem. However, it is a challenging task to reliably detect cracks on damaged surfaces in the real world due to noise and undesired artifacts. Image sensors are widely used for detecting cracks on concrete surfaces to help proactive and timely management of concrete structures. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |