Automatic floor sorting method

Abstract

The invention belongs to a sorting method, and particularly relates to an automatic floor sorting method. The automatic floor sorting method comprises the following steps: step 1, a training stage; artificial intelligence is trained, so that the artificial intelligence can automatically identify the defects of the floor in the black-and-white image and the color image; 2, a use stage; the artificial intelligence obtained through training in the first step is used for recognition, sampling inspection is carried out, and iteration upgrading is continuously carried out on the artificial intelligence. The method has the advantages that an effective recognition algorithm is formed through artificial intelligence training, then the algorithm is used for intelligent recognition, the recognition efficiency is high, the recognition effect is good, the marginal cost is low, and quality control over finished floor products is facilitated. In addition, if other defects occur, the recognition algorithm can be upgraded and reformed only by supplementary training, upgrading and iteration are convenient and fast, and iteration cost is low.

Description

technical field [0001] The invention belongs to a sorting method, in particular to a floor automatic sorting method. Background technique [0002] Under the wave of Industry 4.0, industrial automation is the development trend. In the process of industrial automation, intelligent quality inspection occupies an important part and is a key step to ensure product quality. [0003] In the flooring industry, especially in the field of PVC flooring, appearance directly affects product quality and consumers' willingness to purchase. Therefore, almost all manufacturers attach great importance to the quality inspection of floor appearance. [0004] In traditional technology, the appearance quality inspection has always been done through manual visual screening, relying on experienced workers for visual identification. However, due to the many types of floor defects, such as attached Figure 1-13 shown. These defects include but are not limited to: pits, offsets, discounts, scratche...

AI Technical Summary

Problems solved by technology

During manual identification, as the working hours go by, workers will inevitably experience work fatigue, lack of energy and other problems, so there may be missed detections

In addition, different workers have different judgment standards for the same defect. Some people think it is a defect, while others think it is a small defect, which does not affect subsequent sales and use, so the judgment standard is not uniform.

At the same time, the labor cost is extremely high, and the efficiency of manual detection is also low.

[0005] In summary, the traditional detection method has the following disadvantages: slow detection speed, unstable detection results, uncontrollable detection accuracy, high detection cost, etc.

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