论文标题
使用面膜R-CNN评估汽车损坏
Assessing Car Damage using Mask R-CNN
论文作者
论文摘要
基于图片的车辆保护处理是一个重要的区域，具有巨大的机械化程度。在本文中，我们考虑了车辆伤害表征的问题，其中一部分分类可以是细粒状的。因此，我们研究了基于学习的深刻程序。起初，我们尝试合法地准备CNN。无论如何，由于明显的信息的安排很少，因此它并不能令人钦佩。那时，我们研究了空间显式预先准备的影响，然后进行调整。最后，我们探索有关移动学习和服装学习的不同途径。试验结果表明，移动学习的工作效果优于空间明确的调整。我们完成了89.5％的精确度，融合了移动和收集学习。
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.
