Bridge crack detection using CNN
| Author(s) | : | Manu S. Pawale, Manali P. Adake, Mayuri V. Sadaphule |
| Institution | : | Department of Computer Engineering, Sinhgad College of Engineering, Pune |
| Published In | : | Vol. 7, Issue 7 — July 2020 |
| Page No. | : | 18-21 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
To day of the week, identifying cracks in bridges and responsible bridge conditions primarily involvemanual labour. Bridge inspection by anthropological experts has some drawbacks such in place of the inability tophysically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge superintendent.Additionally it requires proper training of the human resource and complete the aforementioned is not cost effective. Thisarticle proposes an automatic bridge examination attitude manipulating wavelet-based image features lengthwise bymeans of CNN for automatic detection of cracks in conduit images. A two-stage method is followed, someplace in theprincipal stage a decision is finished as whether a doppelgänger should go through a pre-processing step (depending onimage characteristics), and glowing along in the second stage, wavelet features are pull out on or after the image using aupward window-based procedure.
Manu S. Pawale, Manali P. Adake, Mayuri V. Sadaphule, “Bridge crack detection using CNN”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 7, Issue 7, pp. 18-21, July 2020.
