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Using Computer Vision Methods for AlSiC Products Quality Control
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AI
DOI：10.3103/S1060992X25700250.png)
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Abstract
En 中文
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.
Keywords:
computer vision
neural networks
deep learning
composite materials
AlSiC
defects
Journal
O
IF:
0.8
Papers:
65
Citations:
233