| Abstract [eng] |
The inspection of manufacturing processes has become an essential part of industry 4.0 nowadays. Quality assessment at each step of production enables the detection of flaws at early fabrication stages, reducing materials usage, thus cutting manufacturing costs. Furthermore, it mitigates the risk of defects appearing in sold production. Non-invasive check-ups, such as those computer vision (CV) based, might be suitable for most observable defects, as the visual-based approach is routinely employed for defect detection in the industry. In this work, deep learning-based approaches are discussed for complicated surface abnormality of visual or structural consistency analysis. Lightweight convolutional neural network designs and model training approaches are proposed to increase the prediction performance while considering computational performance. The developed methods are investigated in artificially generated surfaces, concrete and asphalt defects, and wooden furniture board drilling segmentation datasets. |
