基于机器视觉的金属丝网缺陷识别方法

目的 提升金属丝网的检测效率与检测精度。方法 提出一种应用于金属丝网表面缺陷识别的EfficientNetV2改进网络,首先更改了网络的骨干结构,在特征提取模块前后分别引入通道拆分与通道转换等操作,以增大网络容量,提高特征利用率;其次重新设计网络的分类器,通过对提取的高级语义信息进行逐层分步压缩,以减小特征损失,提高分类精度;最后搭建图像采集系统,构造金属丝网缺陷数据集。结果 实验结果表明,文中改进的网络模型在数据集上的准确率、精确度和特异度分别达到99.43%、99.42%和99.88%,图像识别耗时为27.5ms,增强了缺陷识别效果。结论 该方法具有较高的准确率,在金属丝网缺陷检测上具有较好的实用性,也可为其他类似产品的缺陷检测提供参考。

Defect Recognition Method of Wire Mesh Based on Machine Vision

The work aims to improve the detection efficiency and accuracy of wire mesh. An improved network EfficientNetV2 for wire mesh surface defect recognition was proposed. Firstly, the backbone structure of the network was changed, and operations such as channel splitting and channel conversion were introduced before and after the feature extraction module to increase network capacity and improve feature utilization. Secondly, the classifier of the network was redesigned, and the extracted high-level semantic information was compressed layer by layer to reduce the feature loss and improve the classification accuracy. Finally, an image acquisition system was built to construct a wire mesh defect data set. According to the experimental results, the accuracy, precision and specificity of the improved network model on the data set were 99.43%, 99.42% and 99.88% respectively, and the image recognition time was 27.5 ms, which enhanced the defect recognition effect. The method has high accuracy and good practicability in wire mesh defect detection, and can also provide reference for defect detection of other similar products.