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Abstract <p>This paper considers the problem of detecting surface defects based on a small number of labeled images. The method consists of two stages. At the first stage, all areas that are suspected of containing image defects are found using a selective search algorithm. At the second stage, image recognition is performed in the selected areas based on a Siamese neural network. The method uses multiple images of defects of the same class to train the network. The method is based on training a network to match tasks between similar data. Both images are processed by convolutional subnetworks. The features of both images are calculated. The difference between two feature vectors is calculated. Sigmoid activation is used to determine whether images belong to the same class. In the study of the developed method, the mean average precision indicator was 91.3%, which satisfies industrial applications. The advantage of the presented method is that training occurs on a small set of images. This makes it possible to significantly reduce the costs of data collection and processing.</p>

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Surface Defect Detection Method Based on a Small Number of Labeled Images

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • A. A. Zakharov,
  • M. N. Shamshin,
  • A. L. Zhiznyakov

摘要

Abstract

This paper considers the problem of detecting surface defects based on a small number of labeled images. The method consists of two stages. At the first stage, all areas that are suspected of containing image defects are found using a selective search algorithm. At the second stage, image recognition is performed in the selected areas based on a Siamese neural network. The method uses multiple images of defects of the same class to train the network. The method is based on training a network to match tasks between similar data. Both images are processed by convolutional subnetworks. The features of both images are calculated. The difference between two feature vectors is calculated. Sigmoid activation is used to determine whether images belong to the same class. In the study of the developed method, the mean average precision indicator was 91.3%, which satisfies industrial applications. The advantage of the presented method is that training occurs on a small set of images. This makes it possible to significantly reduce the costs of data collection and processing.