Literature review on the use of deep learning focused on automated optical inspection systems for surface defect detection in the manufacturing sector
DOI:
https://doi.org/10.18779/ingenio.v6i2.680Keywords:
manufacturing, deep learning, convolutional neural networks, optimization algorithms, automated optical inspection, generalization, imbalanceAbstract
The manufacturing industry uses supervised machine learning methodologies to improve inspection processes through machine vision. Automated optical inspection offers efficiency in the inspection process for the detection of defects in the manufacture of various products. This work contributes with the identification of those limitations in data processing based on defined rule sets and process domain management. A literature review is proposed on the use of deep learning focused on automated optical inspection systems for the detection of surface defects in the manufacturing sector. The proposed objective is to identify the different architectures oriented on convolutional neural networks applied in optical inspection systems to automate the extraction of features or patterns. By means of the exploration of relevant works, a total of 47 selected papers that address generalization problems and optimization techniques are identified. Finally, the information of the different architectures is contrasted for the elaboration of a comparative table that evidence improvements in the accuracy of the optical inspection systems by means of the percentage achieved. These results contribute as an input to the existing body of literature for improvements in the manufacturing sector.
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Y.-T. Li, P. Kuo, and J.-I. Guo, “Automatic Industry PCB Board DIP Process Defect Detection System Based on Deep Ensemble Self-Adaption Method,” IEEE Trans Compon Packaging Manuf Technol, vol. 11, no. 2, pp. 312–323, 2021, doi: 10.1109/TCPMT.2020.3047089.
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L. C. Chen et al., “Edge-glued wooden panel defect detection using deep learning,” Wood Sci Technol, vol. 56, no. 2, pp. 477–507, Mar. 2022, doi: 10.1007/S00226-021-01316-3/TABLES/9.
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