摘要
针对平板陶瓷膜表面缺陷实时检测时存在检测准确率较低的问题,本文提出了一种融合坐标注意力和自适应特征的YOLOv5陶瓷膜缺陷检测方法。通过在原有YOLOv5模型的主干网络中加入坐标注意力机制,建立位置信息和通道之间的关系,从而更准确地获取感兴趣区域。在原始网络的预测网络中融入自适应特征融合机制,提高模型对多尺度缺陷的检测能力。将空洞空间卷积池化金字塔模块替换原始网络中的空间金字塔池化模块,提高卷积核视野获取更多的有用信息。实验结果表明:本文模型平均精度为97.8%,检测帧数为32 FPS,平均精度与原始YOLOv5模型相比提高了5.5%。本文提出的模型在满足平板陶瓷膜缺陷的实时检测条件下,提高了模型的检测准确率,对推动平板陶瓷膜缺陷检测的发展具有一定的参考价值。
To address the problem of low detection accuracy in real-time detection of defects on the surface of flat ceramic films,this paper proposes a YOLOv5 ceramic film defect detection method that incorporates coordinate attention and adaptive features.By adding a coordinate attention mechanism to the backbone network of the original YOLOv5 model,the relationship between location information and channels is established to obtain the region of interest more accurately.The adaptive feature fusion mechanism is incorporated into the prediction network of the original network to improve the detection capability of the model for multi-scale defects.Replace the spatial pyramid pooling module in the original network with the spatial pyramid pooling module of the null space convolution pooling module to improve the convolutional kernel field of view to obtain more useful information.The experimental results show that the average accuracy of this model is 97.8%,the number of detection frames is 32 FPS,and the average accuracy is improved by 5.5% compared with the original YOLOv5 model.The model proposed in this paper improves the detection accuracy of the model under the condition of satisfying the real-time detection of flat ceramic film defects,which has certain reference value for promoting the development of flat ceramic film defect detection.
作者
雷震霆
朱兴龙
孙进
马昊天
梁立
游志刚
Lei Zhenting;Zhu Xinglong;Sun Jin;Ma Haotian;Liang Li;You Zhigang(School of Mechanical Engineering,Yangzhou University,Yangzhou 225127,China)
出处
《电子测量技术》
北大核心
2023年第7期133-137,共5页
Electronic Measurement Technology
基金
国家自然科学基金(51775484,51475409)
2022年扬州市科技计划项目(YZ2022184)
2021年扬州市产业前瞻与共性关键技术项目(YZ2021020)
2020年江苏省产学研合作项目(BY2020663)
2020年扬州大学市校合作专项(YZ2020166)资助。
