A Universal Tire Detection Method Based on Improved YOLOv8
Driving safety has become one of the top concerns of people in contemporary society, with regular tire inspection being indispensable to ensure safe driving. However, traditional methods of tire defect detection have encountered problems such as slow detection speed, complex tire defect backgrounds,...
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2024-01-01
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| author | Chi Guo Mingxia Chen Junjie Wu Haipeng Hu Luobing Huang Junjie Li |
| author_facet | Chi Guo Mingxia Chen Junjie Wu Haipeng Hu Luobing Huang Junjie Li |
| author_sort | Chi Guo |
| collection | DOAJ |
| description | Driving safety has become one of the top concerns of people in contemporary society, with regular tire inspection being indispensable to ensure safe driving. However, traditional methods of tire defect detection have encountered problems such as slow detection speed, complex tire defect backgrounds, and limited hardware resources. To address the above problems, this paper proposes a lightweight YOLOv8n-SOI algorithm for tire defect detection. First, a similarity-based attention mechanism (SimAM) was introduced to the C2f block of the backbone network to improve the ability to extract the shape features of irregular tire defects in complicated backdrops. Subsequently, four network structures were created in response to the need for lightweighting the detection procedure by incorporating the omni-dimensional dynamic convolution (ODConv) network structure at various positions. The network structure with the highest detection accuracy and smaller model parameters was chosen. Finally, Inner-intersection over union (Inner-IOU), an auxiliary edge loss function that concentrated more on the centre of the target, was added to the modified network to speed up tire defect regression convergence. Experimental results demonstrated that YOLOv8n-SOI surpassed the YOLOv8n algorithm regarding precision, recall, and mean average precision (mAP) by 3.8%, 1.1%, and 3%, respectively. Additionally, YOLOv8n-SOI reduced floating-point operations (FLOPs) by 6% and had a model size of only 6.5MB. This article presents the latest you only look once (YOLO) model for tire identification, contributing to the advancement of research in tire defect detection and providing a level of assurance for driving safety. |
| format | Article |
| id | doaj-art-b97b17f1e03c45708c566288aba8764e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b97b17f1e03c45708c566288aba8764e2025-08-20T01:54:11ZengIEEEIEEE Access2169-35362024-01-011217477017478110.1109/ACCESS.2024.345615610669573A Universal Tire Detection Method Based on Improved YOLOv8Chi Guo0https://orcid.org/0009-0001-9081-8548Mingxia Chen1Junjie Wu2Haipeng Hu3Luobing Huang4Junjie Li5Education Department of Guangxi Zhuang Autonomous Region, Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology, Guilin, ChinaEducation Department of Guangxi Zhuang Autonomous Region, Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology, Guilin, ChinaSchool of Electronic Information Engineering, Liuzhou Polytechnic University, Liuzhou, ChinaEducation Department of Guangxi Zhuang Autonomous Region, Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology, Guilin, ChinaEducation Department of Guangxi Zhuang Autonomous Region, Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology, Guilin, ChinaEducation Department of Guangxi Zhuang Autonomous Region, Key Laboratory of Advanced Manufacturing and Automation Technology, Guilin University of Technology, Guilin, ChinaDriving safety has become one of the top concerns of people in contemporary society, with regular tire inspection being indispensable to ensure safe driving. However, traditional methods of tire defect detection have encountered problems such as slow detection speed, complex tire defect backgrounds, and limited hardware resources. To address the above problems, this paper proposes a lightweight YOLOv8n-SOI algorithm for tire defect detection. First, a similarity-based attention mechanism (SimAM) was introduced to the C2f block of the backbone network to improve the ability to extract the shape features of irregular tire defects in complicated backdrops. Subsequently, four network structures were created in response to the need for lightweighting the detection procedure by incorporating the omni-dimensional dynamic convolution (ODConv) network structure at various positions. The network structure with the highest detection accuracy and smaller model parameters was chosen. Finally, Inner-intersection over union (Inner-IOU), an auxiliary edge loss function that concentrated more on the centre of the target, was added to the modified network to speed up tire defect regression convergence. Experimental results demonstrated that YOLOv8n-SOI surpassed the YOLOv8n algorithm regarding precision, recall, and mean average precision (mAP) by 3.8%, 1.1%, and 3%, respectively. Additionally, YOLOv8n-SOI reduced floating-point operations (FLOPs) by 6% and had a model size of only 6.5MB. This article presents the latest you only look once (YOLO) model for tire identification, contributing to the advancement of research in tire defect detection and providing a level of assurance for driving safety.https://ieeexplore.ieee.org/document/10669573/YOLOv8tire defect detectionattention mechanismlight weightdeep learning |
| spellingShingle | Chi Guo Mingxia Chen Junjie Wu Haipeng Hu Luobing Huang Junjie Li A Universal Tire Detection Method Based on Improved YOLOv8 IEEE Access YOLOv8 tire defect detection attention mechanism light weight deep learning |
| title | A Universal Tire Detection Method Based on Improved YOLOv8 |
| title_full | A Universal Tire Detection Method Based on Improved YOLOv8 |
| title_fullStr | A Universal Tire Detection Method Based on Improved YOLOv8 |
| title_full_unstemmed | A Universal Tire Detection Method Based on Improved YOLOv8 |
| title_short | A Universal Tire Detection Method Based on Improved YOLOv8 |
| title_sort | universal tire detection method based on improved yolov8 |
| topic | YOLOv8 tire defect detection attention mechanism light weight deep learning |
| url | https://ieeexplore.ieee.org/document/10669573/ |
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