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A lightweight detection algorithm for tooth cracks in optical images

computers in Biology and Medicine 2024年 182卷 109153-109153页

作者： Xie, Zewen Hu, Xian Guo, Lide Lin, Weiren Liu, Jiakun Zhang, Chunliang Ge, Guanghua Tang, Yadong Wang, Wenlong School of Mechanical and Electrical Engineering Guangzhou University Guangzhou510006 China School of Physics and Material Science Guangzhou University Guangzhou510006 China School of Computer Science and Cyber Engineeringe Guangzhou University Guangzhou510006 China Department of Dentistry Hospital of Guangdong University of Technology Guangdong University of Technology Guangzhou510006 China School of Biomedical and Pharmaceutical Sciences Guangdong University of Technology Guangzhou510006 China

Objectives: Cracked tooth syndrome (CTS) is one of the major causes of tooth loss, presents the problem of early microcrack symptoms that are difficult to distinguish. This paper aims to investigate the practicality and feasibility of an improved object detection algorithm for automatically detecting cracks in dental optical images. Methods: A total of 286 teeth were obtained from Sun Yat-sen University and Guangdong University of Technology, and simulated cracks were generated using thermal expansion and contraction. Over 3000 images of cracked teeth were collected, including 360 real clinical images. To make the model more lightweight and better suited for deployment on embedded devices, this paper improves the YOLOv8 model for detecting tooth cracks through model pruning and backbone replacement. Additionally, the impact of image enhancement modules and coordinate attention modules on optimizing our model was analyzed. Results: Through experimental validation, we conclude that that model pruning reduction maintains performance better than replacing a lightweight backbone network on a tooth crack detection task. This approach achieved a reduction in parameters and GFLOPs by 16.8 % and 24.3 %, respectively, with minimal impact on performance. These results affirm the effectiveness of the proposed method in identifying and labeling tooth fractures. In addition, this paper demonstrated that the impact of image enhancement modules and coordinate attention mechanisms on YOLOv8's performance in the task of tooth crack detection was minimal. Conclusions: An improved object detection algorithm has been proposed to reduce model parameters. This lightweight model is easier to deploy and holds potential for assisting dentists in identifying cracks on tooth surfaces. © 2024 Elsevier Ltd

关键词： YOLOv8 Object detection Optical image Cracked tooth detection Lightweight algorithm

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