多注意力机制金字塔池化金手指划痕分割方法

Multi-Attention Mechanism Pyramid Pooling Method for Scratch Segmentation of Gold Finger

针对传统图像处理方法和基于深度学习的分类模型对金手指表面划痕检测效果不理想的情况,提出了一种多注意力机制金字塔池化方法对金手指表面划痕进行语义分割。采用ResNet50模型获取输入图像的特征图;在金字塔的不同层中将特征图分成大小不同的子区域,然后对每个子区域进行平均池化操作;池化后的特征图加入多种注意力机制来提取关键部分的特征信息,并使用边界细化模块对边缘区域进一步精细化,提高分割准确度。通过上采样,将四种不同尺寸的特征图采用级联的方式对划痕区域进行特征融合;与带有整体信息的特征图拼接后经过卷积操作得到最后的预测结果。实验结果表明,本文采用的方法较其他常用分割模型在MIOU和MPA指标上具有明显提升,分别达到86.03%和94.35%,具有一定的应用价值。

Aiming at the situation that the traditional image processing method and the classification model based on deep learning are not ideal for the surface scratch detection of golden finger,a multi-attention mechanism Pyramid pooling method is proposed for semantic segmentation of scratches on the surface of gold fingers.Firstly,the ResNet50 model is used to obtain the feature map of the input image,the feature map is divided into sub-regions of different sizes in different layers of the Pyramid pooling,and then performs an average pooling operation on each sub-region.The pooled feature map adds a variety of attention mechanisms to extract the feature information of the key part,and uses the boundary refinement module to further refine the edge region,and improve segmentation accuracy.Through up-sampling,the feature maps of four different sizes are feature fused using a cascade method.After splicing with the feature maps with overall information,the final prediction result is obtained through convolution operations.The experimental results show that the method used in this paper has a significant improvement in MIOU and MPA indicators compared with other commonly used segmentation models,reaching 86.17%and 94.47%respectively,which has certain application value.