Machine learning books and papers

Forwarded from Papers

Title:
CNN-based Labelled Crack Detection for Image Annotation
 
 
Short title:

Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection
 
Abstract
Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.
 
Field
Mechanical Engineering, Material Engineering, Industrial Engineering, Computer Engineering, Civil Engineering, Aerospace Engineering
Journal
1. Optics and Laser Technology (8.3 CiteScore, 5.0 Impact Factor)
2. Optics and Lasers in Engineering (9.3 CiteScore, 4.6 Impact Factor)
3. The International Journal of Advanced Manufacturing Technology (3.4 CiteScore, 3.226 Impact Factor)
 
با عرض سلام نفرات ١ تا ٤ اين مقاله جهت ارسال به ژورنال خالي مي باشد. دوستاني كه نياز دارند به ايدي بنده پيام بدن.

@Raminmousa

@paper4money

www.tg-me.com/us/Machine learning books and papers/com.Machine_learn/2713

6.2K viewsApr 12, 2024 at 11:05

Title:
CNN-based Labelled Crack Detection for Image Annotation
 
 
Short title:

Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection
 
Abstract
Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.
 
Field
Mechanical Engineering, Material Engineering, Industrial Engineering, Computer Engineering, Civil Engineering, Aerospace Engineering
Journal
1. Optics and Laser Technology (8.3 CiteScore, 5.0 Impact Factor)
2. Optics and Lasers in Engineering (9.3 CiteScore, 4.6 Impact Factor)
3. The International Journal of Advanced Manufacturing Technology (3.4 CiteScore, 3.226 Impact Factor)
 
با عرض سلام نفرات ١ تا ٤ اين مقاله جهت ارسال به ژورنال خالي مي باشد. دوستاني كه نياز دارند به ايدي بنده پيام بدن.

@Raminmousa

@paper4money

BY Machine learning books and papers