摘要
针对车载导航导光板图像纹理背景复杂渐变、亮度不均匀、缺陷细微且类型多等特点,并根据导光板光学特性、网点排列、缺陷成像效果等,结合轻量化与级联深度学习网络提出了一种缺陷快速检测方法。首先,根据导光板缺陷分布特点,通过改进卷积层连接与特征图下采样的方法,设计一轻量化二分类网络实现疑似缺陷区域的快速分割;其次,利用改进的ResNet网络构建多分类网络,并提出两阶段网络级联的方法,对分割的疑似缺陷区域提取多样化特征实现缺陷的精确分类;然后,采用固定窗口在完整导光板图像上滑动,将滑动窗口图像裁剪后批量输入级联网络进行缺陷的粗定位与分类;最后,利用工业现场采集的导光板图像自建数据集,并以此为基础进行了大量实验。实验结果表明:与其他导光板缺陷检测算法相比,本文算法在准确率与检测时间上得到显著提升,检测平均准确率达到98.4%,单张检测时间提升到1.95 s,准确率、实时性均达到工业检测要求。
According to the characteristics of complex gradient,uneven brightness,subtle defects,and multiple types of car navigation light guide plate image texture background,and according to the optical characteristics of light guide plate,dot arrangement,defect imaging effect,etc.,a fast defect detection method combined with lightweight and cascaded deep learning network is proposed.First,based on the characteristics of defect distribution of the light guide plate,by improving the convolutional layer connection and the down-sampling method of the feature map,a lightweight two-classification network was designed to quickly segment the solid line suspected defect area.Second,the improved ResNet network was used to construct a multi-classification network.The lightweight network and the multi-classification network were cascaded and merged,and diversified features were extracted from the segmented suspected defect regions to achieve accurate defect classification.Then,defect region could be located and recognized by predicting images which were from fixed windows sliding on the completed light guide plate.Finally,a self-built dataset of light guide plate images collected from the industrial field was used,and a large number of experiments are carried out on this basis.Experimental results show that the average accuracy of the detection algorithm for light guide plate defects detection is 98.4%,and the single detection time is 1.95 s.The accuracy and real-time performance meet the requirements of industrial detection.
作者
李俊峰
何炎森
戴文战
Li Junfeng;He Yansen;Dai Wenzhan(School of Mechanical Engineering and Automation,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;School of Information and Electronic Engineering,Zhejiang Gongshang University,Hangzhou,Zhejiang 310018,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第14期188-198,共11页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61374022)
浙江省公益性技术应用研究计划项目(LGG18F030001,GG19F030034)。
