Assessing Car Damage using Mask R-CNN
Sarath P.1, Soorya M.2, Shaik Abdul Rahman A.3, S. Suresh Kumar4
1Sarath P., Student, Dept of CSE, Rajalakshmi Engineering College, Chennai, TN, India.
2Soorya M., Student, Dept of CSE, Rajalakshmi Engineering College, Chennai, TN, India.
3Shaik Abdul Rahman A., Student, Dept of CSE, Rajalakshmi Engineering College, Chennai, TN, India.
4S. Suresh Kumar*, Associate Professor, Dept of CSE, Rajalakshmi Engineering College, Chennai, TN, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2287-2290 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5302029320/2020©BEIESP | DOI: 10.35940/ijeat.C5302.029320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Picture based vehicle protection handling is a significant region with enormous degree for mechanization. In this paper we consider the issue of vehicle harm characterization, where a portion of the classifications can be fine-granular. We investigate profound learning based procedures for this reason. At first, we attempt legitimately preparing a CNN. In any case, because of little arrangement of marked information, it doesn’t function admirably. At that point, we investigate the impact of space explicit pre-preparing followed by tweaking. At last, we explore different avenues regarding move learning and outfit learning. Trial results show that move learning works superior to space explicit tweaking. We accomplish precision of 89.5% with blend of move and gathering learning.
Keywords: CNN, VGG-16,Deep Learning ,Car Damage Detection