摘要
针对芯片缺陷检测中,缺陷尺寸跨度大、特征相似、小目标难识别、漏检等问题,本文提出基于YOLOv5改进的缺陷检测方法。针对小目标缺陷检测中出现的漏检、误检等问题,提出新增小目标特征检测器(small target feature detector,S-Detector),提升模型对小目标缺陷的学习能力;针对缺陷尺寸跨度大、特征相似等问题,提出具有高效聚焦学习能力的特征金字塔结构(efficient attention feature pyramid networks,EA-FPNs),提升模型对不同尺寸缺陷的检测能力;针对预测阶段冗余框较多导致时间开销大的问题,提出基于面积的边界框融合算法(bounding box fusion algorithm,BFA),减少冗余框。实验结果表明,本文方法相较于改进前,检测精确度提升1.2%,小目标缺陷精确度提升1.6%;采用BFA消除冗余框的同时,平均检测时长为26.8μs/张,较使用BFA前减少了5.2μs。本文所提方法具有良好性能,能够提升检测效率。
To address the problems of large defect size span,similar characteristics,difficulty in recognition of small targets,and missed objects in chip defect detection,an improved method based on YOLOv5 is proposed.To solve missed and false detection of small targets,we presented a new small target feature detector(S-Detector)to improve the learning capability of the model.For the large defect size span and similar characteristics,efficient attention feature pyramid networks(EA-FPNs)with highly active focus learning ability are proposed to improve the ability to detect different sizes of defects.The bounding box fusion algorithm(BFA)is developed to reduce the redundant boxes and time overhead in prediction.The experimental results show that the detection accuracy of this method is enhanced by 1.2%and the accuracy of minor target defects is improved by 1.6%;while using BFA to eliminate the redundant boxes,the detection time of a single image is 26.8μs,which is decreased by 5.2μs before BFA.The proposed method has good performance and efficiency in chip defect detection.
作者
张恒
程成
袁彪
赵洪坪
吕雪
杭芹
Zhang Heng;Cheng Cheng;Yuan Biao;Zhao Hongping;Lyu Xue;Hang Qin(School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第5期36-45,共10页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金项目(12005030)
重庆市自然科学基金(cstc2021jcyj-bsh0252)
磁约束聚变安徽省实验室开放基金(2021AMF01004)项目资助。
作者简介
张恒,2009年于中南大学获得学士学位,2014年于中国科学院大学获得硕士学位,2020年于中国科技大学获得博士学位,现为重庆邮电大学副教授,主要研究方向为人工智能、特种视觉和计算成像。E-mail:zhangheng@cqupt.edu.cn;通信作者:杭芹,2009年于中南大学获得学士学位,2015年于中南大学获得硕士学位,2019年于中国科技大学获得博士学位,现为重庆邮电大学讲师,主要研究方向为人工智能、特种视觉和计算成像。E-mail:hangqin@cqupt.edu.cn。
