FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images

FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images

Crack inspections of automotive engine components are usually conducted manually; this is often tedious, with a high degree of subjectivity and cost. Therefore, establishing a robust and efficient method will improve the accuracy and minimize the subjectivity of the inspection. This paper presents a...

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Bibliographic Details
Main Authors: Michael Abebe Berwo, Yong Fang, Jabar Mahmood, Nan Yang, Zhijie Liu, Yimeng Li
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Applied Sciences
Subjects:
deep learning
crack classification and detection
transfer learning
fine-tuning
data augmentation
YOLOv5s object detector
Online Access:https://www.mdpi.com/2076-3417/12/19/9713

Internet

https://www.mdpi.com/2076-3417/12/19/9713

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