基于深度学习的螺纹钢表面缺陷检测

螺纹钢是土建工程中必不可少的建筑材料, 在轧制过程中因受轧辊磨损、钢坯质量等因素影响, 导致表面缺陷, 如不能及时发现就会生产出大量废品, 严重影响企业经济效益. 本文提出一种基于深度学习的螺纹钢缺陷检测方法, 通过生产现场工业相机采集螺纹钢图像, 对表面缺陷进行分类标记, 建立样本数据集, 利用深度卷积对抗生成网络DCGAN对数据集增强. 采用Faster RCNN构建螺纹钢缺陷检测模型, 利用迁移学习方法实现小样本螺纹钢表面缺陷检测, 通过对损失函数、优化方法、学习率、滑动平均参数的设置来评估优化螺纹钢缺陷检测模型. 实验表明所设计的方法具有较好的稳定性和实用性, 能有效地解决人工检测过程中效率低、误检率高等问题.

Defect Detection Method of Rebar Based on Deep Learning

The rebar represents an essential material in civil engineering. In the rolling process, roll wear, billet quality, and other factors will cause surface defects. If they cannot be detected in time, a large number of waste products will be produced, seriously affecting the economic benefits of enterprises. In this study, a detection method of rebar defects based on deep learning is proposed. Images of rebars are collected by industrial cameras in the production site, and their surface defects are classified and labeled to establish sample datasets that are further enhanced by the Deep Convolutional Generative Adversarial Network (DCGAN). Faster RCNN is adopted to construct the detection model of rebar defects, which can identify the surface defects in a small sample size with migration learning. In addition, the detection model is optimized through the evaluation of the setting of loss function, optimization methods, learning rates and sliding average. The experiment reveals that the method can effectively solve the problems of low efficiency and high false detection rates caused by manual detection, with good stability and practicability.