论文标题
使用深度学习方法进行自动进度监控的材料识别
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
论文作者
论文摘要
人工智能的最新进步，尤其是深度学习，通过引入最先进的自动化方法来不可逆转地改变了许多领域。施工监测不是例外。作为建筑监测系统的一部分，材料分类和认可引起了深度学习和机器视觉研究人员的关注。但是，为了创建准备生产的系统，仍然有一条较长的覆盖道路。为了创建强大的系统，需要解决现实世界中的问题，例如不同的照明和达到可接受的准确性。在本文中，我们已经解决了这些问题，并达到了最先进的表现，即此任务的准确率为97.35％。此外，收集并公开发表了一个新的数据集，其中包含从几个建筑地点拍摄的11个类图像的1231张图像，以帮助该领域的其他研究人员。
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation. Construction monitoring has not been an exception; as a part of construction monitoring systems, material classification and recognition have drawn the attention of deep learning and machine vision researchers. However, to create production-ready systems, there is still a long path to cover. Real-world problems such as varying illuminations and reaching acceptable accuracies need to be addressed in order to create robust systems. In this paper, we have addressed these issues and reached a state of the art performance, i.e., 97.35% accuracy rate for this task. Also, a new dataset containing 1231 images of 11 classes taken from several construction sites is gathered and publicly published to help other researchers in this field.
