Chip Surface Defect Detection Algorithm Based on Improved YOLOv3
Abstract
Chip package testing is a key process to eliminate bad products in the electronics industry. Aiming at the problems of YOLOv3 chip detection in complex environments with low accuracy and large number of model parameters, an improved YOLOv3 automatic detection method based on EMO, called EMO-YOLOV3, was proposed. This method uses EMO to replace the backbone of Darknet53 in YOLOv3, inherits the efficiency of CNN to model short-range dependencies and the dynamic modeling capability of Transformer to learn long-distance interactions. The results show that the model has a very good detection effect on 7 kinds of environment, such as character defect, scratch defect and braid damage. Compared with the original YOLOv3 model, the mAP of chip surface defect detection is improved by about 3.4% while the number of model parameters is reduced. Therefore, it is considered that this method can be used for real-time automatic detection of chip surface defects.
References
[1]
Jiao Yong. Key Constraints of high-quality development of digital economy and the path of big countries [J],Qinghai Social Sciences,2022,No.253(01):92-97.
[2]
Liu Chen, Yuan Xiaofang, Tian Zhengming, Wang Haoran. research on chip defect detection algorithm based on point pattern matching[J].LASER JOURNAL,2020,41(01):39-44.
[3]
Wang Xinyu, Jiang Sanxin.Overview of Chip Defect Detection[J].Modern Manufacturing Technology and Equipment,2022,58(05):94-98.
[4]
Luo H, Peng S, Chen H K, et al.Review on latest research progress of challenging problems in object detection[J].Computer Engineering and Applications, 2021, 57(5):36-46.
[5]
Ren S, He K, Girshick R, Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28.
[6]
Liu W, Anguelov D, Erhan D, Ssd: Single shot multibox detector[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer International Publishing, 2016: 21-37.
[7]
Redmon J, Divvala S, Girshick R, et al.You only look once:Unified, real- time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), June 27- 30, 2016: 6517-6525
[8]
Redmon J, Farhadi A.YOLO9000:Better, faster, stronger[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), July 21-26, 2017:6517-6525.
[9]
Zhou Tianyu, Zhu Qibing, Huang Min Defect Detection of Chip on Carrier Based on Lightweight Convolutional Neural Network[J]. Computer Engineering and Applications,2022,58(07):213-219. (in Chinese)
[10]
Yefan Zhou, "Rescarch on Imagc-Based Automatic Wafer Surface Defect Detection Algorithm," Journal of lmage and Graphics,Vol. 7,No. 1, pp. 26-31,March 2019.
[11]
Teow Wee Teo and Mohd Zaid Abdullah, "Solar Cell Micro-Crack Detection UsingLocalised Texture Analysis,"Journal of Image and Graphics,Vol. 6,No. 1, pp.54-58,June2018.
[12]
Liu Yanju, Wang Qiuji, Zhang Huiyu, Liu Yanzhong, Zhao Kaifeng.Defect Detection of Workpiece Surface Based on Improved SSD[J/OL].Hot Working Technology,2024(02):134-139[2023-07-19].https://doi.org/10.14158/j.cnki.1001-3814.20211221.
[13]
Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.
[14]
Li Guojin, Huang Xiaojie, Li Xiuhua, Detection model of wine grapes using lightweight network MobileNetV2[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(17):168-176+317.
[15]
Zhang J, Li X, Li J, Rethinking Mobile Block for Efficient Neural Models[J]. arXiv preprint arXiv:2301.01146, 2023.
[16]
Vaswani A, Shazeer N, Parmar N, Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
Index Terms
- Chip Surface Defect Detection Algorithm Based on Improved YOLOv3
Recommendations
Anomaly Detection Model for Key Places Based on Improved YOLOv5
Artificial Intelligence and SecurityAbstractIn recent years, key places such as underground stations and train stations, which are crowded and highly mobile, have become key targets for abnormal behaviour such as violence by some extremists or violent elements. The public safety risks in ...
Water Surface Target Detection Based on Improved YOLOv3 in UAV Images
ICCBN '21: Proceedings of the 2021 9th International Conference on Communications and Broadband NetworkingIn order to better manage and protect rivers and lakes, the most important requirement is to find the objects on the surface of rivers and lakes in time. Generally, image segmentation and target detection are used to detect water surface targets. The ...
Power Transmission Line Foreign Object Detection based on Improved YOLOv3 and Deployed to the Chip
MLMI '20: Proceedings of the 2020 3rd International Conference on Machine Learning and Machine IntelligenceThe application of object detection is becoming more and more widely in various fields, including the power industry, of course. And YOLOv3 is one of the most popular algorithms in the field of object detection owing to its high performance and ...
Comments
Information & Contributors
Information
Published In
November 2023
237 pages
ISBN:9798400709272
DOI:10.1145/3638682
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
VSIP 2023
VSIP 2023: 2023 the 5th International Conference on Video, Signal and Image Processing
November 24 - 26, 2023
Harbin, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 24Total Downloads
- Downloads (Last 12 months)24
- Downloads (Last 6 weeks)1
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format