Defect inspection is one of the most critical tasks in the industry as it can reduce
risks of production stops and assure quality control. In recent years, multiple
industries have been adopting computer vision systems, especially based on deep
learning techniques, as their main detection methods to improve efficiency, reduce
risks and human resources, and enhance real-time performance. However, its adoption
in the industry is still limited by the labor-intense and time-consuming process of
collecting high-quality custom training datasets. At the same time, many industries
have access to the CAD models of the components they want to detect or classify as
part of the design process. Taking this into account, in the present work, we analyze
the performance of various image classification models to visually detect defects
in production. Our method systematically generates synthetic datasets from CAD
models using Blender to train neural networks under different settings. The proposed
method shows that image classification models benefit from a diversity of the range of
defect values during training but struggle to identify low-resolution defects, even for
state-of-the-art architectures like Vision Transformer and ConvNext or SqueezeNet,
which proved to have comparable performance to these networks. Similarly, adding
background, texture, and camera pose to training examples provides more contextual
information to image classification models but does not necessarily help them detect
the defects accurately. Finally, we observed that using a unique tolerance value for
all flange pipe sizes can negatively impact the detection accuracy because, for larger
pipe flanges, minor defects are not as perceptible as for small flanges.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/676424 |
| Date | 04 1900 |
| Creators | Toro Gonzalez, Frankly L. |
| Contributors | Ghanem, Bernard, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Ahmed, Shehab, Feron, Eric |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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