亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5

鉴定(生物学) 材料科学 磁力轴承 粒子(生态学) 磁粉探伤 方位(导航) 计算机科学 结构工程 机械工程 磁性纳米粒子 人工智能 工程类 纳米技术 地质学 磁铁 海洋学 植物 纳米颗粒 生物
作者
Yun Yang,Jinzhao Zuo,Long Li,Xianghai Wang,Zijian Yin,Xingyun Ding
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (6): 065405-065405 被引量:2
标识
DOI:10.1088/1361-6501/ad3181
摘要

Abstract The fluorescent magnetic particle inspection technique is often used for surface crack detection of bearing rings due to its advantages of simple operation and high sensitivity. With the development of computer vision technology, more and more visual algorithms are used in magnetic particle inspection for defect detection. However, most of these current algorithm models have low detection accuracy and poor efficiency, making it difficult to meet the precision requirements of production testing and affecting the overall pace of production processes. To address this problem, this paper proposes an improved algorithm model based on Yolov5. Firstly, MobileNetV3-small is utilized to construct the backbone feature extraction network, reducing the network’s parameter count and enhancing its detection speed. In addition, Bidirectional Feature Pyramid Network is implemented to facilitate swift and efficient multi-scale feature fusion, while the C3 module in the neck is replaced with C2f to enhance detection precision. Finally, Focal-Loss EIoU is adopted as the loss function to improve the model’s accuracy in positioning the crack borders. Experimental results demonstrate that the precision of this model in detecting surface cracks in bearing rings achieves an impressive 95.1%, while the recall reaches 90.4%. The mAP stands at 0.946. When compared to the original Yolov5s network, this model showcases a reduction in network parameters by 32.1% and a significant increase in frames per second by 40.0%. These improvements effectively fulfill the production process’s demands for crack detection tasks, providing a balance between accuracy and efficiency.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ding应助panpan采纳,获得10
2秒前
5秒前
追云断月完成签到,获得积分10
5秒前
8秒前
11秒前
zimo33完成签到,获得积分10
12秒前
14秒前
15秒前
一杯橙完成签到,获得积分10
16秒前
17秒前
外向不愁发布了新的文献求助10
20秒前
22秒前
yyds发布了新的文献求助10
23秒前
25秒前
27秒前
善学以致用应助外向不愁采纳,获得10
29秒前
30秒前
一只爱学习的蘑菇完成签到,获得积分10
38秒前
42秒前
47秒前
52秒前
tinner完成签到,获得积分10
1分钟前
qql完成签到,获得积分10
1分钟前
1分钟前
科目三应助科研通管家采纳,获得10
1分钟前
1分钟前
1分钟前
Daodao发布了新的文献求助10
1分钟前
曲蔚然发布了新的文献求助30
1分钟前
1分钟前
wanci应助高高的无敌采纳,获得10
1分钟前
Daodao完成签到,获得积分20
1分钟前
panpan发布了新的文献求助10
1分钟前
丘比特应助panpan采纳,获得10
1分钟前
自觉的万言完成签到 ,获得积分10
1分钟前
加油杨完成签到 ,获得积分10
1分钟前
1分钟前
傲娇的蛋挞完成签到,获得积分10
1分钟前
领导范儿应助wuxiaojiao采纳,获得20
1分钟前
1分钟前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
1.3μm GaAs基InAs量子点材料生长及器件应用 1000
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3526437
求助须知:如何正确求助?哪些是违规求助? 3106931
关于积分的说明 9281882
捐赠科研通 2804416
什么是DOI,文献DOI怎么找? 1539468
邀请新用户注册赠送积分活动 716571
科研通“疑难数据库(出版商)”最低求助积分说明 709546