摘要
磁瓦表面缺陷样本数量少,异常视觉特征分布发散,现有依赖目标特征的有监督检测方法不能有效检测未定义缺陷;磁瓦表面正常纹理呈非均匀且非周期性分布,使得经典重构网络难以准确地重构磁瓦表面正常特征,导致相关无监督检测方法性能低下.为此,采用多头注意力增强的掩码图像修复网络(MIINet),长距离提取图像特征,捕捉全局信息,增强图像修复的能力;引入视觉显著性算法抑制磁瓦表面纹理信息和突显缺陷区域,以便二值化算法精准分割缺陷嫌疑区域;利用MIINet修复待检测图像缺陷嫌疑区域,选用修复前后图像的残差图像和结构相似性实现缺陷检测与缺陷判定.与经典无监督方法相比,修复缺陷嫌疑区域的表面缺陷检测方法的准确率提升了2.36%,F1值提升了1.62%.
The number of magnetic tiles with surface defects is limited,and abnormal visual features are diversely distributed.The existing supervised detection methods that rely on target features cannot effectively detect undefined defects.The non-uniform and non-periodic distribution of normal texture on the surface of magnetic tiles makes it difficult for classical reconstruction networks to accurately reconstruct the normal features,resulting in poor performance of related unsupervised detection methods.The multi-head attention-based masked image inpaint network(MIINet)was utilized to extract image features over long distances,capture global information and enhance the repair capability of images.The vision saliency algorithm was used to suppress the texture information of the magnetic tile surface and emphasize the defect area,enabling the binary value algorithm to accurately segment the suspected defect region.MIINet was utilized to repair the suspected defect region in the image.The residual image and structural similarity of the before and after repair images were selected to achieve defect detection and defect judgment.Compared with the classical unsupervised method,the accuracy of the proposed surface defect detection method for repairing the suspected defect area was increased by 2.36%,and the F1 value was increased by 1.62%.
作者
唐善成
逯建辉
张莹
金子成
赵安新
TANG Shancheng;LU Jianhui;ZHANG Ying;JIN Zicheng;ZHAO Anxin(College of Communication and Information Technology,Xi’an University of Science and Technology,Xi’an 710054,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第4期718-728,共11页
Journal of Zhejiang University：Engineering Science
基金
国家重点研发计划资助项目(2018YFC0808300)
陕西省科技计划重点产业创新链(群)项目(2020ZDLGY15-07)。
关键词
多头注意力
磁瓦表面缺陷检测
无监督学习
图像修复
视觉显著性
multi-head attention
magnetic tile surface defect detection
unsupervised learning
image inpaint-ing
vision saliency
