计算机辅助设计
计算机科学
人工智能
表面粗糙度
深度学习
目标检测
工程制图
图像(数学)
软件
骨干网
模式识别(心理学)
机器学习
计算机视觉
工程类
程序设计语言
物理
量子力学
计算机网络
作者
Hao Hu,Chao Zhang,Yanxue Liang
标识
DOI:10.1007/s12206-021-1125-8
摘要
Engineering drawing inspection is important to CAD modeling of mechanical parts. Traditional inspection methods mainly rely on manual analysis by using the CAD software, which requires expert knowledge and massive time. In view of simplifying the analysis for non-experts and improving detection efficiency and accuracy, this study proposes a generic approach combining object detection and image recognition methods to identify surface roughness of mechanical drawings. For both the object detection and image recognition methods, deep learning models with different backbone networks are trained and tested independently. Experimental results show that a combination of Faster-RCNN with ResNet101 as backbone network, and SSD with ResNet50 as backbone network achieves the best performance under our evaluation metrics.
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