FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images
نویسندگان
چکیده
Crack inspections of automotive engine components are usually conducted manually; this is often tedious, with a high degree subjectivity and cost. Therefore, establishing robust efficient method will improve the accuracy minimize inspection. This paper presents approach towards crack classification, using transfer learning fine-tuning to train pre-trained ConvNet model. Two deep convolutional neural network (DCNN) approaches training classifier—namely, via (1) Light architecture from scratch, (2) fined-tuned top layers architectures AlexNet, InceptionV3, MobileNet—are investigated. Data augmentation was utilized over-fitting caused by an imbalanced inadequate sample. improved index 4%, 5%, 7%, respectively, for proposed four approaches. The achieved better recall precision scores. fine-tuned features MobileNet attained classification thus classifiers. Moreover, we employed up-to-date YOLOv5s object detector detect region. We obtained mean average (mAP) 91.20% on validation set, indicating that model effectively distinguished diverse part cracks.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12199713