| dc.description.abstract | During and after completing conventional manufacturing processes, quality control
is conducted with a view to confirming the integrity of the products. Vision-based
inspection systems have been extensively adopted in numerous industries associated with
the smart factory concept due to improvements in precise and fast inspection. Computer
vision is also known for its drastic reduction of the human inspection cost. This paper
focuses primarily on the development of four models for the inspection of casting products
that is supported by convolutional neural networks and transfer learning techniques in deep
learning, thereby enabling the classification of products with or without defects. The
performance of the proposed algorithm for inspecting casting products has been validated
using more than 700 images of casting products, resulting in more than 98% prediction
accuracy and instant prediction time. | en_US |
