摘要
佩戴不同材质、型号的口罩对病毒传播的防控效果各不相同,对图像数据集中的口罩进行分割,将有助分析不同类型的口罩在防控效果上的差异。当前,面部口罩检测算法较多,但缺乏进一步的分割,为此文中提出一种基于图像处理和深度学习的面部口罩分割方法。文中所提方法是一种改进的对比度自适应直方图均衡化预处理方法,其通过亮度自适应调整减少因部分图像过暗导致的传统预处理效果不佳的影响。以SSD(Single Shot MultiBox Detector)进行口罩预检测,对结果以CA-Net(Comprehensive Attention Convolutional Neural Networks)进行口罩分割。CA-Net以U-Net为骨干网络,增加了空间注意模块、通道注意模块和尺度注意模块以便同时实现关于特征地图的空间、通道和比例的综合注意力引导,突出空间位置、通道和尺度。使用该方法初步分割结果的Dice系数评价指标可以达到79.18%±3.44%;增加预处理和后处理操作后,Dice系数可提升至84.03%±2.81%,表明文中所提方法能够明显改善视觉分割结果。
Wearing medical masks of various materials and types will lead to different prevention and control the effect of virus spread.Segmentation to masks in image data sets will benefit analyzing the differences in prevention and control effects of different types of masks.Currently,there are many facial mask detection algorithms,but there is a lack of further segmentation.Therefore,this study proposes a facial mask segmentation method based on image processing and deep learning.An improved contrast-limited adaptive histogram equalization preprocessing method is proposed to reduce the poor effect of traditional preprocessing caused by dark part of the image through brightness adaptive adjustment.SSD network is used for mask pre-detection,and CA-Net is used for mask segmentation.With U-Net as the backbone network,CA-Net adds spatial attention module,channel attention module and scale attention module to realize the comprehensive attention guidance of space,channel and scale of feature map at the same time.The segmentation result is processed by binary image closing operation to get the final segmentation results.The Dice evaluation of preliminarily segmentation index can reach to 79.18%±3.44%,and the Dice coefficient is increased to 84.03%±2.81%by adding preprocessing and post-processing operations,indicating that the proposed method could significantly improve the visual segmentation results.
作者
李珂然
陈胜
柯盼盼
LI Keran;CHEN Sheng;KE Panpan(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《电子科技》
2023年第6期64-71,共8页
Electronic Science and Technology
基金
国家自然科学基金(81101116)。
