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Abstract
The low accuracy of detection algorithms is one impediment in detecting ceramic tile’s surface defects online utilizing intelligent detection instead of human inspection. The purpose of this paper is to present a CNFA for resolving the obstacle. Firstly, a negative sample set is generated online by non-defective images of ceramic tiles, and a comparator based on a modified VGG16 extracts a reference image from it. Disguised rectangle boxes, including defective and non-defective, are acquired from the image to be inspected by a detector. A reference rectangle box most similar to the disguised rectangle box is extracted from the reference image. A discriminator is constituted with a modified MobileNetV3 network serving as the backbone and a metric learning loss function strengthening feature recognition, distinguishing the true and false of disguised and reference rectangle boxes. Results exhibit that the discriminator appears to have an accuracy of 98.02%, 13% more than other algorithms. Furthermore, the CNFA performs an average accuracy of 98.19%, and the consumption time of a single image extends by only 64.35 ms, which has little influence on production efficiency. It provides a theoretical and practical reference for surface defect detection of products with complex and changeable textures in industrial environments.
Highlights
With the rapid development of infrastructure construction in the past two decades, ceramic tile (CT) production capacity has increased significantly and has gradually become an essential industry for national economic growth [1]
True Positive (TP): defective CTs are correctly identified as defective; False Positive (FP): non-defective CTs are incorrectly identified as defective; False Negative (FN): defective CTs are incorrectly identified as non-defective; True Negative (TN): non-defective CTs are correctly identified as non-defective
The following conclusions can be drawn from Table 6: the number of defect TP is lower than the reference TP, and the number of defect FN is greater than the reference FN, indicating that the probability of misrecognizing a defect as a texture is greater than that of misrecognizing a texture as a defect
Summary
With the rapid development of infrastructure construction in the past two decades, ceramic tile (CT) production capacity has increased significantly and has gradually become an essential industry for national economic growth [1]. CT production mainly includes raw materials mixing and grinding, dehydration, firing, inkjet, and polishing. During this period, mechanical or glaze defects such as corner/edge cracks, pinholes, delaminations, glaze bubbles, and scratches, etc., will inevitably appear on the surface. Defect detection is a key link to prevent defective products from entering the market. Workers who observe fast-moving CTs on the assembly line under bright light for a long time are prone to visual fatigue, which induces many problems, such as missed defect inspections, defect types and dimensions that cannot be quantified, and statistical deviations in product qualification rates.
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