余永维,杜柳青,闫 哲,许贺作.基于深度学习特征的铸件缺陷射线图像动态检测方法[J].农业机械学报,2016,47(7):407-412.
Yu Yongwei,Du Liuqing,Yan Zhe,Xu Hezuo.Dynamic Detection of Casting Defects Radiographic Image Based on Deep Learning Feature[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(7):407-412.
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基于深度学习特征的铸件缺陷射线图像动态检测方法   [下载全文]
Dynamic Detection of Casting Defects Radiographic Image Based on Deep Learning Feature   [Download Pdf][in English]
投稿时间:2016-04-07  
DOI:10.6041/j.issn.1000-1298.2016.07.055
中文关键词:  铸件; 缺陷检测  射线图像  深度学习  注意机制
基金项目:重庆市基础与前沿研究计划基金项目(cstc2013jcyjA70009)和国家自然科学基金青年基金项目(51075419)
作者单位
余永维 重庆理工大学 
杜柳青 重庆理工大学 
闫 哲 重庆理工大学 
许贺作 重庆理工大学 
中文摘要:针对X射线检测中铸件微弱缺陷误检率和漏检率高的问题,提出一种基于选择性注意机制和深度学习特征匹配的缺陷动态跟踪检测方法。基于射线图像序列,采取帧内注意区域检测消除漏检、帧间深度学习特征匹配跟踪排除误检的策略。在帧内检测阶段,提出通过中央-周边梯度搜索方法模拟生物视觉的中央-周边差运算,根据梯度阈值直接检测各可疑缺陷区域,无需分割出缺陷本身。在帧间跟踪阶段,借鉴人类大脑视觉感知系统的深度学习层次结构,建立基于卷积神经的深度学习网络,可疑缺陷区域灰度信号直接作为输入,自动抽取表征可疑缺陷区域的本质特征信息,组成深度学习特征矢量。定义基于欧氏距离的特征矢量相似度,通过连续图像中可疑缺陷区域的相似度匹配实现缺陷跟踪,以消除噪声等伪缺陷。实验结果表明,基于深度学习特征匹配方法的铸件缺陷图像动态检测,误检率和漏检率均低于3%,缺陷检测准确率超过97%,证明了所提方法的有效性。
Yu Yongwei  Du Liuqing  Yan Zhe  Xu Hezuo
Chongqing University of Technology,Chongqing University of Technology,Chongqing University of Technology and Chongqing University of Technology
Key Words:casting  defect detection  radiographic image  deep learning  visual attention mechanism
Abstract:In order to reduce the misdetection ratio and false detection ratio of small casting defects in X radiographic testing,a dynamic defects tracking and detection method based on selective attention mechanism and deep learning feature matching was proposed. The misdetection of image sequences was eliminated with attention region detection of individual images and the false detection was also eliminated with feature matching among the image sequence. In the phase of individual images detection, a search method based on central-peripheral gradient was proposed to simulate the central-peripheral difference operation of biological vision. And the gradient threshold was defined. Then by comparing each regional gradient with threshold, the suspicious defect area was directly detected according to the gradient threshold. The defects did not need to be segmented from the suspicious image area. So the method avoided the great influence of the defects segmentation accuracy rate to defects tracking. In the phase of tracking among the image sequence,referencing to the deep learning hierarchy of human visual perception system, a deep learning network based on convolution neural was established. The gray level signal of the suspicious defect area was directly used as input. The network could automatically extract the essential feature which made up the deep learning feature vector. The similarity of feature vector was defined based on Euclidean distance. Defect tracking was achieved by similarity matching of suspicious defect regions in continuous frames. Then the noise and other false defects were eliminated. The experiments show that the false detection rate and the misdetection rate are less than 3%. The detection accuracy rate is more than 97%,which proved the method is advanced and effective.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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