| 吴永泽,俞建峰,化春键,蒋毅,钱陈豪.基于改进YOLOv8n的圆柱电池壳多维缺陷检测技术研究[J].电子测量与仪器学报,2024,38(12):62-71 |
| 基于改进YOLOv8n的圆柱电池壳多维缺陷检测技术研究 |
| Research on multi-dimensional defect detection technology forcylindrical battery shells based on improved YOLOv8n |
| |
| DOI： |
| 中文关键词: 圆柱电池壳 多维缺陷检测 YOLOv8n 可切换空洞卷积 LSKA注意力 |
| 英文关键词:cylindrical battery shell multi-dimensional defect detection YOLOv8n switchable atrous convolution LSKA attention |
| 基金项目:国家自然科学基金（51905215）项目资助 |
|
|
| Author | Institution |
| Wu Yongze | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment and Technology, Wuxi 214122, China |
| Yu Jianfeng | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment and Technology, Wuxi 214122, China |
| Hua Chunjian | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment and Technology, Wuxi 214122, China |
| Jiang Yi | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment and Technology, Wuxi 214122, China |
| Qian Chenhao | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment and Technology, Wuxi 214122, China |
|
| 摘要点击次数: 505 |
| 全文下载次数: 368 |
| 中文摘要: |
| 圆柱电池壳的多维缺陷检测是保证锂电池质量和安全的关键技术。由于生产加工和运输过程中涉及的工艺环节不同，圆柱电池壳的每个部位均会产生缺陷。为解决现有检测方法在处理种类繁多、尺度不一的圆柱电池壳缺陷时检测精度低的问题，本文根据圆柱电池壳各部位特征搭建图像采集装置，构建了圆柱电池壳多维缺陷数据集，提出了一种基于改进YOLOv8n的圆柱电池壳多维缺陷检测技术。首先，引入可切换空洞卷积改进C2f模块，增强多尺度特征提取能力；其次，结合平均池化和最大池化策略改进下采样模块，在降低特征图空间尺寸的同时保留关键信息；最后，引入LSKA注意力机制，增强多尺度特征的融合效果。实验结果表明，改进后的YOLOv8n模型在自制的圆柱电池壳缺陷数据集上平均检测精度可达77.4%，相较于原始模型提升了4.3%，计算量下降了17%，模型大小仅为6 MB，检测速度达到177 FPS，满足工业大批量实时检测的要求。 |
| 英文摘要: |
| The multi-dimensional defect detection of cylindrical battery shells is a critical technology for ensuring the quality and safety of lithium batteries. Due to the different processes involved in production and transportation, defects may occur in each part of the cylindrical battery shells. To solve the problem of low detection accuracy in existing methods when handling the diverse and variably scaled defects of cylindrical battery shells, this study designs an image acquisition system based on the characteristics of each part of the battery shells and constructs a multi-dimensional defect dataset. Additionally, a multi-dimensional defect detection technology is proposed based on an improved YOLOv8n. Firstly, the switchable atrous convolution is used in the C2f module to improve the multi-scale feature extraction capability. Secondly, the down sampling module is refined by combining average pooling and max pooling strategies, reducing the spatial dimensions of feature maps while retaining key information. Finally, the LSKA attention mechanism is introduced to enhance the fusion effect of multi-scale features. Experimental results show that the improved YOLOv8n model achieves an average detection accuracy of 77.4% on a custom cylindrical battery shell defect dataset, which is 4.3% higher than the original model. Furthermore, the computational load is reduced by 17%, the model size is only 6 MB, and the detection speed reaches 177 FPS, meeting the requirements for real-time industrial mass detection. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
