| Research on Tire Damage Detection Algorithm Based on Improved YOLOv7 Model |
| Received:July 03, 2023 Revised:July 03, 2023 |
| DOI：10.12136/j.issn.1000-890X.2025.03.0226 |
| Key Words: tire；YOLOv7 model；damage detection；attention mechanism；loss function；deep learning technology |
| Author Name | Affiliation | E-mail | | JIA Shu’an | Qingdao University of Technology | jiashuan0202@126.com | | CAO Jinfeng* | Qingdao University of Technology | caojinfeng@qut.edu.cn | | CAO Yingjie | Qingdao University of Technology | | | PENG bo | Qingdao University of Technology | | | XUE Maolin | Qingdao University of Technology | | | ZHENG Jianfeng | Qingdao University of Technology | |
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| Abstract: |
| The tire damage detection model based on YOLOv7 improved model（referred to as TD-YOLO model） was proposed.The TD-YOLO model integrated a parameter free convolutional neural network attention mechanism（SimAM） to enhance the model’s feature learning ability，improved the loss function to enhance the accuracy and sensitivity of detecting bulges，cracks and embedded foreign objects，and introduced space to deep（SPD） module layers at the output of the network structure to construct new convolutional neural network（CNN） module to improve the accuracy of small target damage detection.The average detection accuracy of the TD-YOLO model was 0.916，which was 0.069 larger than the YOLOv7 model.The comprehensive performance of the TD-YOLO model was good，it had good promotion and application values. |
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