Automatic quality assessment of formed fiber products via Computer Vision and Artificial Intelligence

Abstract: Defects on fiber products have varied appearances and are common in production lines. A reliable system that can classify and identify defects without subjectivity and fatigue can improve a company's quality management. Computer vision systems are crucial for any autonomous system, but accuracy is essential for real-life applications. This study aims to investigate the contribution of computer vision through computer vision and artificial intelligence in detecting defects in formed fiber products. A hand-crafted dataset of four common defects from the production line was created and tested using transfer learning. The system's performance was measured in terms of mean average precision (mAP), precision, and recall, resulting in a performance of 81.8% mAP, 0.84 recall rate, and 0.79 precision rate for the hand-crafted dataset.