EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application-Reference-Cited by-同舟云学术

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EHA-YOLOv5: An Efficient and Highly Accurate Improved YOLOv5 Model for Workshop Bearing Rail Defect Detection Application

Published:2024 Issue: Volume:12 Page:81911-81924
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hu Jiyong¹,Yang Hongfei²^ORCID,He Jiatang¹,Bai Dongxu³^ORCID,Chen Hongda³

Affiliation:

1. FAW-Volkswagen Automobile Company Ltd., Changchun, Jilin, China

2. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China

3. School of Instrumentation Science and Electrical Engineering, Jilin University, Changchun, China

Funder

Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10552769.pdf?arnumber=10552769

Reference28 articles.

1. New directions for inline inspection of automobile laser welds using non-destructive testing

2. Laser Ultrasonic inspection of a Wire + Arc Additive Manufactured (WAAM) sample with artificial defects

3. Intelligent denoise laser ultrasonic imaging for inspection of selective laser melting components with rough surface

4. A Characteristic Approximation Approach to Defect Opening Profile Recognition in Magnetic Flux Leakage Detection

5. A novel magnetic flux leakage method based on the ferromagnetic lift-off layer with through groove

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