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Abstract <p>This paper explores the application of computer vision for quality control of products, focusing on the challenge of surface defect detection with a limited dataset. Substrate made of AlSiC composite material is a good example. To address the small sample size, data augmentation and transfer learning techniques have been employed, pre-training a model on a public crack dataset. The core of approach is utilization of the YOLOv8-OBB object detector, chosen for its support of oriented bounding boxes, which are crucial for accurately capturing elongated defects like cracks. Furthermore, to enhance detection reliability, a method that combines results from multiple images of the same object captured from different angles has been proposed. This multi-view analysis allows for a reduction in the detection confidence threshold, increasing the true positive rate. Therefore, offered technique in article is a combination of YOLOv8-Obb, Augmentation, Transfer Learning and Multi-View Analysis. The proposed system was tested on a dedicated dataset of AlSiC products, achieving a defect detection rate of over 80% with a false alarm probability of approximately 1%. The results demonstrate the feasibility of using modern neural network-based detectors for automated visual inspection in specialized industrial applications.</p>

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Using Computer Vision Methods for AlSiC Products Quality Control

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • V. E. Dementyev,
  • A. G. Tashlinskii,
  • I. V. Chufarov,
  • A. P. Tereshenok,
  • S. N. Potapov

摘要

Abstract

This paper explores the application of computer vision for quality control of products, focusing on the challenge of surface defect detection with a limited dataset. Substrate made of AlSiC composite material is a good example. To address the small sample size, data augmentation and transfer learning techniques have been employed, pre-training a model on a public crack dataset. The core of approach is utilization of the YOLOv8-OBB object detector, chosen for its support of oriented bounding boxes, which are crucial for accurately capturing elongated defects like cracks. Furthermore, to enhance detection reliability, a method that combines results from multiple images of the same object captured from different angles has been proposed. This multi-view analysis allows for a reduction in the detection confidence threshold, increasing the true positive rate. Therefore, offered technique in article is a combination of YOLOv8-Obb, Augmentation, Transfer Learning and Multi-View Analysis. The proposed system was tested on a dedicated dataset of AlSiC products, achieving a defect detection rate of over 80% with a false alarm probability of approximately 1%. The results demonstrate the feasibility of using modern neural network-based detectors for automated visual inspection in specialized industrial applications.