| 吴凡凡,张鹏超,王磊,曾昆峰,崔金凯,马文星.基于改进YOLOv11n的微特电机电枢缺陷检测算法[J].电子测量与仪器学报,2025,39(12):167-177 |
| 基于改进YOLOv11n的微特电机电枢缺陷检测算法 |
| Improved YOLOv11n-based armature defect detectionalgorithm for microtome motors |
| |
| DOI： |
| 中文关键词: 电枢 缺陷检测 YOLOv11n C3K2-MSPFA OD-ADown DIoU损失函数 |
| 英文关键词:armature defect detection YOLOv11n C3K2-MSPFA OD-ADown DIoU loss function |
| 基金项目:国家自然科学基金（62176146）项目资助 |
|
|
| Author | Institution |
| Wu Fanfan | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;
2.Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Zhang Pengchao | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;
2.Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Wang Lei | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;
2.Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Zeng Kunfeng | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;
2.Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Cui Jinkai | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;
2.Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, China |
| Ma Wenxing | 1.School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;
2.Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, China |
|
| 摘要点击次数: 694 |
| 全文下载次数: 524 |
| 中文摘要: |
| 针对现有微特电机电枢表面缺陷检测方法存在检测精度不高，特别是相似性工件容易误判等问题，结合深度学习的方法，提出了一种基于改进YOLOv11n的微特电机电枢外观缺陷检测方法。首先，采用高效部分卷积和残差连接思想，设计一种部分多尺度特征聚合模块（C3K2-multi scale partial feature aggregation，C3K2-MSPFA），显著提高了对不同尺度目标的检测能力，提升了模型的检测精度；其次，引入全维动态卷积（omni-dimensional dynamic convolution，ODConv）和自适应下采样（adaptive downsampling，ADown），设计一种轻量化的全维下采样模块（omni-dimensional adaptive downsampling，OD-ADown），减少了C3K2-MSPFA模块的参数量和计算量；最后，为了弥补完整交并比损失函数(complete-IoU loss，CIoU)在检测任务中泛化性弱和收敛速度慢的问题，使用距离交并比损失函数(distance-IoU loss，DIoU)提高模型精度，加快边界框回归速度。在自建数据集上进行实验对比，实验结果表明，改进后的模型平均精度达到94.2%，召回率为90.9%，准确率为95.9%，参数量为2.15×106，模型大小为4.5 MB。与原YOLOv11n网络模型相比，准确率、召回率、平均精度均值分别提高了1.3%、4.6%、2.7%，参数量和模型大小相比于原模型分别降低了16.67%和15.09%。能够满足移动端和嵌入式设备的部署要求，为微特电机电枢表面缺陷检测的发展提供了一定有效的技术支持。 |
| 英文摘要: |
| To address the issues of low detection accuracy and misclassification of similar components in existing micro-motor armature surface defect detection methods, this study proposes an improved YOLOv11n-based approach for detecting surface defects in micro-motor armatures by integrating deep learning techniques. First, by adopting the concepts of efficient partial convolution and residual connections, we designed a partial multi-scale feature aggregation module named C3K2-multi scale partial feature aggregation (C3K2-MSPFA). This significantly enhances the detection capability for objects at different scales, thereby improving the model’s detection accuracy. Second, we introduce omni-dimensional dynamic convolution (ODConv) and adaptive downsampling (ADown) to design a lightweight omni-dimensional adaptive downsampling (OD-ADown) module, reducing the parameter count and computational load of the C3K2-MSPFA module. Finally, to address the weak generalization and slow convergence issues of complete-IoU loss (CIoU) in detection tasks, we employ distance-IoU (DIoU) loss to enhance model accuracy and accelerate bounding box regression speed. Experiments were conducted on a self-built dataset, and the results showed that the improved model achieved an average accuracy of 94.2%, a recall rate of 90.9%, an accuracy rate of 95.9%, 2.15×106 parameters, and a model size of 4.5 MB. Compared with the original YOLOv11n network model, the accuracy, recall, and average precision have been improved by 1.3%, 4.6%, and 2.7%, respectively. Compared with the original model, the number of parameters and model size were reduced by 16.67% and 15.09%, respectively. It can meet the deployment requirements of mobile and embedded devices, and provide certain effective technical support for the development of surface defect detection of armatures in micro and special electric machines. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
