Abstract
A Machine-Vision Based defect detection method for the inspection of industrial fabric product appearance is proposed. Through the cooperation of mechanical arms and machine vision, a series of fabric appearance defect detections are performed, including weaving uniformity detection and damage detection. The industrial camera calibration algorithm is basically used to integrate the industrial camera with the robotic arm system. For the software, Gaussian curve fitting is basically used to detect the uniformity of the appearance of the fabric. Instead of manual visual inspection, the test and verification of produced products are used to solve industrial recruitment difficulties and manual fatigue, heavy workload and other issues. In the process, the algorithm was optimized based on actual production test conditions, we proposed a quadratic judgment optimized algorithm which using constrained nonlinear fitting, considering pixel distribution under different ROI setting. The experimental results demonstrate that the recognition success rate of the proposed algorithm can reach 98.2%, UPH can reach 300 pcs. It can meet the production’s requirement and has been applied to actual production lines and achieved good results.
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Cao, Y. (2021). Machine-Vision Based Defect Detection Technology for Industrial Fabric Alignment. In: Zhen, D., et al. Proceedings of IncoME-V & CEPE Net-2020. IncoME-V 2020. Mechanisms and Machine Science, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-75793-9_55
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DOI: https://doi.org/10.1007/978-3-030-75793-9_55
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