基于改进分割决策网络的铝材表面凹坑缺陷检测

Aluminum surface pit defect detection method based on improved segmentation decision network

针对工业铝材表面缺陷检测准确度低、模型泛化性差的问题,提出了一种基于改进分割决策网络的多任务特征融合检测方法.首先针对工业上常见的凹坑缺陷,构造了本文的缺陷检测数据集;其次在原有分割检测模型的基础上添加铝材区域分割网络,从网络结构上减少了模型对样本的需求;并引入挤压激活(Squeeze-and-Excitation)模块,使得模型更聚焦于与铝材缺陷相关的维度特征,提升网络的检测精度.最后在铝材表面凹坑缺陷数据集上进行对比试验,结构表明,所提方法相较于原模型,分割精度提升了1.6%,分类精度提升了4.01%;相较于FCN、U-net网络在图像分割部分检测精度领先0.76%,相较于VGG、Resnet50网络在图像分类部分检测精度领先1.12%.实验证明,该方法在铝材表面凹坑缺陷检测上,具有足够的检测精度,并且稳定、高效.

Aiming at the problems of low accuracy and poor generalization of industrial aluminum surface defect detection,this paper proposes a multi task feature fusion detection method based on improved segmentation decision network.Firstly,aiming at the common pit defects in industry,it constructs the defect detection data set of this paper;secondly,on the basis of the original segmentation detection model,it also adds the aluminum area segmentation network to reduce the demand for samples in the network structure;in addition,the Squeeze-and-Excitation module is introduced to make the model focus more on the dimensional features related to aluminum defects and improve the detection accuracy of the network.Finally,a comparative experiment is carried out on the data set of dirty spots on the aluminum surface.The structure shows that the segmentation accuracy of the proposed method is improved by 1.6%and the classification accuracy is improved by 4.01%compared with the original model;compared with FCN and u-net network,the detection accuracy of image segmentation is 0.76%higher than that of FCN and U-Net network,and 1.12%higher than that of VGG and Resnet50 network.The experimental results have proved that this method has sufficient detection accuracy in the detection of dirty spots on the aluminum surface,and is stable and efficient.