Defect Detection on 3D Print Products and in Concrete Structures Using Image Processing and Convolution Neural Network
- 1 North Carolina A&T State University, United States
Copyright: © 2020 Selorm Garfo, M.A. Muktadir and Sun Yi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This paper explores the automated detection of surface defects on 3-D printed products and concrete structures. They are the main factors to evaluate their quality in addition to dimension and roughness. Traditional detection by human inspectors is far from satisfactory. Manual inspection is time-consuming, error-prone and often leads to loss of resources. For this purpose, image processing and deep learning-based object detection adopted by Google Cloud Machine Learning (ML) Engine is used to detect surface defects. In the case of image processing, two approaches are presented in this paper. In both cases, pixels are being considered to differentiate a smooth or rough surface from a picture taken by a USB camera. For the deep learning- based solution, MobileNet -a base convolution neural network treated as an image feature extractor in combination with Single Shot MultiBox Detector (SSD) as an object detector hence MobileNet-SSD. The model was successfully trained on the Google Cloud ML Engine with the dataset of 20000+ images. The review of the results confirms that with the help of MobileNet-SSD can automatically detect surface defects more accurately and rapidly than conventional deep learning methods.
- Image Processing
- Machine Learning
- Tensor Flow
- 3-D Printing
- Additive Manufacturing