Abstract
Quality of products is the most important factor in manufacturing. Machine vision is a technique that mainly performs human cognitive judgment in the industrial field or performs a task that is generally difficult for a human. However the detection of traditional methods of scanning with human eyes has many difficulties due to repetitive tasks. Recently, an artificial intelligence machine vision has been studied to improve these problems. Using the vision inspection system, it is possible to collect information such as the number of products, defect detection, and types without human intervention, which maximizes the operation-al efficiency of a company such as productivity improvement, quality improvement, and cost reduction. Most of the vision inspection systems currently in use are single-sided images, which collect and inspect one image of the product. However, in the actual manufacturing industry, products that are valid for single-sided image inspection are limited to some product groups, and most require multi-sided image inspection. In addition, the inspection system used in the field must meet the production speed required by the actual manufacturing site and inspect the defects of the product. In this paper, we propose a deep neural network-based vision inspection system that satisfies the multi-sided image inspection and fast production speed of products. By implementing seven cameras and optical technology, multi-sided images of the product are collected simultaneously, and a defect in the product can be quickly detected in real time using a PANet (Path Aggregation Network) model. Through the proposed system, it is possible to inspect product defects at the level required at the manufacturing site, and the information obtained in the inspection process will be used as a very important data to evaluate and improve product quality.
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Han, Y., Jeong, J. (2020). Real-Time Inspection of Multi-sided Surface Defects Based on PANet Model. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_45
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DOI: https://doi.org/10.1007/978-3-030-58802-1_45
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