金怡君,李振宇,杨絮.基于机器视觉的啤酒金属盖表面缺陷检测方法[J].包装工程,2023,44(11):259-267.
JIN Yi-jun,LI Zhen-yu,YANG Xu.Surface Defect Detection of Beer Metal Covers Based on Machine Vision[J].Packaging Engineering,2023,44(11):259-267. |
| 基于机器视觉的啤酒金属盖表面缺陷检测方法 |
| Surface Defect Detection of Beer Metal Covers Based on Machine Vision |
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| DOI：10.19554/j.cnki.1001-3563.2023.11.030 |
| 中文关键词: 机器视觉 金属盖 表面缺陷 改进的YOLO–v5模型 |
| 英文关键词: machine vision metal cover surface defect improved YOLO-v5 model |
| 基金项目:2018年江苏省高校哲学社会科学研究基金项目(2018SJA2268);常州市第十五届社会科学研究课题重点资助项目(CZSKL–2019A002) |
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| 中文摘要: |
| 目的 为了提高啤酒金属盖表面缺陷检测的精度和准确率，提出一种基于机器视觉的金属盖表面缺陷检测方法。方法 以不同类型的啤酒金属盖表面缺陷为研究对象，利用滤波抑噪和高反差保留算法对图像进行处理，运用YOLO–v5网络完成瓶盖的缺陷检测。通过添加注意力机制SE模块、改进模型损失函数和预测框筛选方式等技术手段对原YOLO–v5模型作出优化，抑制图像中的不重要特征，提升小目标检测的准确率和模型的特征提取能力。结果 改进后的YOLO–v5模型与常用的检测模型的对比结果表明，改进YOLO–v5模型在测试集上的mPA指标为93.10%，检测速度达到了294张/min，优势较为明显。结论 针对不同类型的金属盖表面缺陷，基于机器视觉的检测模型均有较高的检测精度和识别准确率，小目标缺陷的漏检率和误检率情况较少，满足生产线实时、高精度的检测要求。 |
| 英文摘要: |
| The work aims to propose a method based on machine vision for surface defect detection of beer metal covers to improve the precision and accuracy of surface defect detection of beer metal covers. Different types of beer metal cover surface defects were used as the research objects. The images were processed by filtering, noise suppression, and high contrast retention algorithms. The YOLO-v5 network was used to complete the defect detection of bottle covers. The original YOLO-v5 model was also optimized by adding the attention mechanism SE module and improving the model loss function and the prediction frame filtering method to suppress the unimportant features in the images and improve the accuracy of small target detection and the feature extraction ability of the model. The comparison between the improved YOLO-v5 model and the commonly used detection model showed that the mPA index of the improved YOLO-v5 model on the test set was 93.10%, and the detection speed reached 294 pieces/min, and the advantages were apparent. For different metal cover surface defects, the detection model based on machine vision has higher detection accuracy and recognition accuracy, and the leakage rate and misdetection rate of minor target defects are low, which meet the real-time and high-precision detection requirements of the production line. |
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