CNN-based Labelled Crack Detection for Image Annotation
This work addresses crack detection for additive manufacturing quality control, but it is incremental as it applies existing CNN techniques to a specific domain without major methodological innovation.
The paper tackles crack detection in additive manufacturing surfaces by proposing a CNN-based method that eliminates extensive feature extraction, achieving 99.54% accuracy on a dataset of 14,982 images with high precision and recall metrics.
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 x 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.