具有复杂纹理的木板表面刮痕缺陷检测模型  

Scratch defect detection model on wooden board surface with complex texture

作　　者：胡勍 秦威[1] 刘成良[1] 石闻天 HU Qing;QIN Wei;LIU Chengliang;SHI Wentian(School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;China Mobile(Shanghai)ICT Co.,Ltd.,Shanghai 200331,China)

机构地区：[1]上海交通大学机械与动力工程学院,上海200240 [2]中移(上海)信息通信科技有限公司,上海200331

出　　处：《计算机集成制造系统》2024年第1期78-89,共12页Computer Integrated Manufacturing Systems

基　　金：教育部—中国移动科研基金资助项目(MCM20180703);上海市科技重大专项资助项目(2021SHZDZX0102);上海市科技创新行动计划资助项目(20511106200)。

摘　　要：为提高木板加工生产线自动化水平,基于Faster RCNN提出一种木板表面刮痕缺陷检测模型,识别和定位不同纹理背景下的木板表面刮痕缺陷。图像预处理阶段提出改进双边滤波算法,在保持刮痕细节特征的同时对纹理背景进行平滑处理;提出灰度自适应刮痕生成方法进行数据增强处理。引入可形变卷积增强模型特征提取能力,使用旋转包围框标注并提出新的包围框回归损失函数,解决水平包围框中刮痕缺陷占比远小于纹理背景的问题。通过实际木板加工生产线采集的图像验证了提出模型的有效性,并将提出的模型与其他缺陷检测方法进行了对比测试,结果证明了所提模型的优越性。To improve the automation level of the wood processing production line,a scratch defect detection model on the wood surface based on Faster RCNN was proposed to identify and locate scratch defects under different texture backgrounds.In the image preprocessing stage,an improved bilateral filtering algorithm was proposed to smooth the texture background while maintaining the details of the scratches.A gray-scale adaptive scratch generation method was proposed for data enhancement.The deformable convolution was introduced to enhance the feature extraction ability of the model,and the rotating bounding box was used and a new bounding box regression loss function was proposed to solve the problem that the proportion of scratch defects in the horizontal bounding box was much smaller than the texture background.The images collected by the actual wood board processing production line verified the effectiveness of the proposed model.The proposed model was compared with other defect detection methods,and the results proved the superiority of the proposed model.

关 键 词：刮痕缺陷 Faster RCNN 可形变卷积 旋转包围框 回归损失 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]