Improved U2Net-Based Surface Defect Detection Method for Blister Tablets

• Published in: Algorithms Vol. 17; no. 10; p. 429
• Main Authors: Zhou, Jianmin, Huang, Jian, Liu, Jikang, Liu, Jingbo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Algorithms; blister tablets; Blistering; Classification; Deep learning; defect detection; Defects; Drug dosages; Drugs; Feature extraction; Gaussian Laplace operator; Good Manufacturing Practice; Image processing; Image segmentation; Laplace transforms; large kernel attention mechanism; Methods; Pharmaceutical industry; Product quality; Quality management; Semantics; Surface defects; Surfaces; Surfaces (Technology); Tablets; U2Net; China
• DOI: 10.3390/a17100429
• ISSN: 1999-4893

Aiming at the problem that the surface defects of blAister tablets are difficult to detect correctly, this paper proposes a detection method based on the improved U2Net. First, the features extracted from the RSU module of U2Net are enhanced and adjusted using the large kernel attention mechanism, so that the U2Net model strengthens its ability to extract defective features. Second, a loss function combining the Gaussian Laplace operator and the cross-entropy function is designed to make the model strengthen its ability to detect edge defects on the surface of blister tablets. Finally, thresholds are adaptively determined using the local mean and OTSU(an adaptive threshold segmentation method) method to improve accuracy. The experimental results show that the method proposed in this paper can reach an average accuracy of 99% and an average precision rate of 96.3%; the model test only takes 50 ms per image, which can meet the rapid detection requirements. Minor surface defects can also be accurately detected, which is better than other algorithmic models of the same type, proving the effectiveness of this method.