CNN-based Labelled Crack Detection for Image Annotation

TL;DR

This paper introduces a CNN-based method for crack detection on Additive Manufacturing surfaces, achieving high accuracy and efficiency without extensive feature engineering or complex image processing.

Contribution

The study presents a deep learning approach that simplifies crack detection by eliminating traditional feature extraction, using CNNs trained with LabelImg and enhanced by OpenCV preprocessing.

Findings

Achieved 99.54% accuracy on a large annotated dataset.

High precision (96%), recall (98%), and F1-score (97%) demonstrate effectiveness.

Simplified crack detection process reduces reliance on traditional IPTs.

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 x 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.