Assessing Car Damage using Mask R-CNN
This work addresses automation in vehicle insurance processing, but it is incremental as it applies existing methods to a specific domain.
The paper tackled vehicle damage classification using deep learning, achieving 89.5% accuracy by combining transfer and ensemble learning after finding that direct CNN training failed due to limited labeled data.
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.