JCSE, vol. 20, no. 3, pp.189-203, September, 2026

DOI: http://dx.doi.org/10.5626/JCSE.2026.20.3.189

Adaptability Analysis of Abutment Tooth Preparations and Fixed Prosthesis Crowns Based on Image Segmentation and Feature Matching Algorithms

Yiping Liu, Xiaoya Su, Jiali Kuang, and Jia Wang
School of Medical Technology, Anyang Vocational and Technical College, Anyang, China Department of Stomatology, Iron Coal General Hospital of Liaoning Health Industry Group, Tieling, China Geriatrics Department, Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China

Abstract: To improve the classification and localization accuracy of spots, wrinkles, uneven edges, filter detachment, and normal samples in cigarette manufacturing, this study investigates a deep vision recognition method. A cigarette production quality control model based on a keypoint detection mechanism is constructed, and a lightweight convolutional structure is combined to reduce redundant computation, enabling the model to perform real-time processing in high-speed production line environments. Results show that the improved model exhibits the best recognition performance, achieving an accuracy of 0.98, precision of 0.97, and recall of 0.96, with significantly improved recognition balance. In the five sample tests, the average accuracy of filter tip separation reaches 0.99, with significant advantages observed in the wrinkle, uneven edge, and spot categories. The research conclusions indicate that this intelligent quality risk control can achieve high-precision identification and stable positioning of cigarette appearance defects, providing effective support for quality risk management in the cigarette industry production process.

Keyword: No keyword

Full Paper:   3 Downloads, 4 View