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| Design and Research of Paper Dirt Determination System Based on Convolutional Neural Network and Machine Vision |
| Received:January 23, 2025 Revised:April 06, 2025 |
| DOI：10.11980/j.issn.0254-508X.2025.08.020 |
| Key Words:paper dirt convolutional neural network (CNN) machine vision image processing |
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| Abstract:This study designed a paper dirt determination system based on convolutional neural network (CNN) and machine vision. The system was constructed with two modules， model training and testing. High-resolution scanners were used to obtain dirt datasets and images of paper samples. Different optimization algorithms were applied to train the classification model， and a diagonal measurement algorithm was adopted. A standard dirt pixel table was created for grading and classification statistics， thereby calculating the dirt. The results showed that the precision of the system could reach 0.007 mm²， which was better than the requirement specified in GB/T 1541—2013 “Paper and board—Determination of dirt”. The classification precision reached 95.89%， enabling full-range determination of various paper products. The repeatability determination error of a single sample was 0. Compared with manual detection， the single-sample detection testing time of the system was reduced by about 97%， realizing efficient and accurate detection of dirt in paper products. |
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