Research on Surface Defect Detection of Camera Module Lens Based on YOLOv5s-Small-Target

Author:

He GangORCID,Zhou Jianyun,Yang HuORCID,Ning Yuan,Zou Huatao

Abstract

For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the problems arising from the anchor frame generated by the network through K-means clustering, the dynamic anchor frame structure DAFS is introduced in the input stage. Secondly, the SPP-D (Spatial Pyramid Pooling-Defect) improved from the SPP module is proposed. The SPP-D module is used to enhance the reuse rate of feature information in order to reduce the loss of feature information due to the maximum pooling of SPP modules. Then, the convolutional attention module is introduced to the network model of YOLOv5s, which is used to enhance the defective region features and suppress the background region features, thus improving the detection accuracy of small targets. Finally, the post-processing method of non-extreme value suppression is improved, and the improved method DIoU-NMS improves the detection accuracy of small targets in complex backgrounds. The experimental results show that the mean average precision mAP@0.5 of the YOLOv5s-Small-Target algorithm is 99.6%, 8.1% higher than that of the original YOLOv5s algorithm, the detection speed FPS is 80 f/s, and the model size is 18.7M. Compared with the traditional camera module lens surface defect detection methods, YOLOv5s-Small-Target can detect the type and location of lens surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals, meeting the demand for real-time and accuracy of camera module lens surface defect detection.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference32 articles.

1. Design of a 360-deg panoramic capture system based on a smart phone

2. Design of Miniaturized Optical Image Stabilization and Autofocusing Camera Module for Cellphones;Chang;Sens. Mater.,2017

3. Surface Defects Detection for Mobilephone Panel workpieces based on Machine Vision and Machine Learning;Huang;Proceedings of the IEEE International Conference on Information and Automation (ICIA),2017

4. A hybrid defect detection method for compact camera lens

5. Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3