Using Computer Vision Methods for AlSiC Products Quality Control

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.