基于CBAM-Unet的铁矿球团边缘分割实验方法

Edge segmentation experimental method of iron ore pellets based on CBAM-Unet

针对高度粘连和堆叠严重的生球影像噪声点多、边界模糊的问题,本文提出一种融入注意力机制的深度学习图像语义分割模型(CBAM-Unet)进行球团边缘检测和分割的方法。方法步骤:(1)样本数据集制备,采集30张包括深度分离、高度粘连到堆叠严重等不同状态的图片,通过数据增广形成900张图片的样本数据集;(2)模型建立,将卷积块状注意力模块(Convolution Block Attention Module,CBAM)融入到Unet网络模型,形成CBAM-Unet模型,对生球边缘进行检测和分割。研究结果表明:相比于Unet方法,CBAM-Unet方法的相似度系数(e_(Dice))、类别平均像素准确率(e_(mPA))、平均交并比(e_(mIoU))分别提高9.8%、9.7%、14.7%。本文提出的识别模型可提高球团分割的准确性,为实际应用奠定基础。

Aiming at the problem of many noisy points and blurred boundaries of highly adherent and heavily stacked fresh pellet images,a method for edge detecting and segmenting of pellets is proposed by a deep learning image semantic segmentation model(CBAM-Unet)that incorporates attention mechanism.The method steps:1)sample data set preparation,collecting 30 images with different states including depth separation,high adhesion to serious stacking,etc.,and forming a sample data set of 900 images by data augmentation;2)model building,and incorporating the Convolution Block Attention Module(CBAM)into the Unet network model to form the CBAM-Unet model for fresh pellet edge detection and segmentation.The results show that,compared with the Unet method,the similarity coefficient(e_(Dice)),the category-average pixel accuracy(e_(mPA)),and the average intersection ratio(e_(mIoU))of CBAM-Unet method are improved by 9.8%,9.7%,and 14.7%,respectively.The recognition model proposed in this paper can improve the accuracy of pellets segmentation and lay a foundation for practical applications.