| Summary: | The midsole is an important part of the shoe, but in its industrial production, the surface quality of the midsole currently relies on manual testing. It cost high and cannot meet the needs of industrial online real-time detection. To realize online surface defect detection, extracting the double edges of the midsole resulting from its special structure becomes an indispensable pre-work. This paper proposes a two-step Otsu method (TT-Otsu) to extract double edges of products. This method adopts the improved Otsu method to process the midsole image in two steps. It respectively combines with the Weighted Object Variance method (WOV) and the Neighborhood Valley-Emphasis method (NVE) to calculate the optimal threshold. Then the image is segmented to extract the edges and the misclassification error (ME) is 0.0007. To ensure the accuracy of the edge detection, the neighborhood gradient extreme value discrimination method is used to realize the local self-checking and make appropriate adjustments to the deviated edge. The false positive rate (FPR) and the false negative rate (FNR) of TT-Otsu are approximately equal to 5%. This method can effectively and clearly extract the double edges of the midsole. The precision rate is 95.61%, the average running time is 1.8s. The experiment demonstrates that the proposed method in this paper has good detection performance and good applicability.
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