基于改进I-Attention U-Net的锌浮选泡沫图像分割算法  

Froth Image Segmentation Algorithm Based on Improved I-Attention U-Net for Zinc Flotation

作　　者：唐朝晖[1] 郭俊岑 张虎[1] 谢永芳[1] 钟宇泽 TANG Zhaohui;GUO Juncen;ZHANG Hu;XIE Yongfang;ZHONG Yuze(School of Automation,Central South University,Changsha 410083,China)

机构地区：[1]中南大学自动化学院,湖南长沙410083

出　　处：《湖南大学学报（自然科学版）》2023年第2期12-22,共11页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(62171476)。

摘　　要：针对泡沫图像的高度复杂性导致其难以被准确分割的难题,本文提出了一种新的I-Attention U-Net网络用于泡沫图像分割.该算法以U-Net网络作为主干网络,使用Inception模块替换第一卷积池化层来提取泡沫图像的多尺度、多层次浅层特征信息;引入金字塔池化模块,通过对不同尺度的特征图求和来提升分割效果;并对自注意力门控单元进行改进,使注意力单元更适合于浮选泡沫图像的分割,强化深层特征的重要性并对不同尺寸的泡沫边界进行强化学习.研究结果表明:本文所提出算法的Jaccard系数为91.73%,Dice系数为95.66%.与同类其他分割算法结果相比,Jaccard系数及Dice系数分别提高了1.59%、0.88%.该模型能够较好地对锌浮选泡沫图像进行分割,解决欠分割与过分割的问题,为后续的泡沫特征提取奠定基础.此外,该方法检测时间和模型参数少,具备可以部署在工业现场计算机的能力,有一定的实际应用价值.To solve the problem that froth image is difficult to be accurately segmented due to its high complexity, this paper proposes a new I-Attention U-Net network for froth image segmentation. The algorithm uses the U-Net network as the backbone network and uses the Inception module to replace the first convolutional pooling layer so as to extract the multi-scale and multi-level shallow feature information of the froth image. A Pyramid pooling module is introduced to improve the segmentation effect by summing the feature maps of different scales. And the self-attention gating unit is improved to make it more suitable for the segmentation of flotation froth images, which strengthens the importance of deep features and performs reinforcement learning on froth edges of different sizes. The research results show that the Jaccard coefficient of the algorithm proposed in this paper is 91.73% and the Dice coefficient is 95.66%.Compared with the results of other similar segmentation algorithms, the Jaccard coefficient and Dice coefficient are increased by 1.59% and 0.88%, respectively. The model can better segment the zinc flotation froth image, and solve the problems of under-segmentation and over-segmentation, which is a good way for the follow-up. In addition, the method has less detection time and fewer model parameters and also has the ability to be deployed in industrial field computers, which has certain practical application value.

关 键 词：泡沫浮选 泡沫图像分割 U-Net Inception模块 增强注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TD952[自动化与计算机技术—计算机科学与技术]