一种基于CA-Net的面部口罩分割方法

doi:10.16180/j.cnki.issn1007-7820.2023.06.010

Abstract

Abstract:

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.

Key words: target detection, HSV space conversion, brightness adaptive adjustment, image segmentation, deep learning, CA-Net, closed operation, SSD

CLC Number:

TP391

LI Keran,CHEN Sheng,KE Panpan. A Method of Facial Mask Segmentation Based on CA-Net[J].Electronic Science and Technology, 2023, 36(6): 64-71.

Figures/Tables 11

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References 28

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检测网络	灵敏度结果/%
SSD	97.84
YOLOv3	95.75
Faster R-CNN	96.63

预处理方法	Dice系数/%
无	79.18±3.44
CLAHE	81.43±2.36
改进的CLAHE	83.86±2.74

训练所用模型	Dice系数/%	分割所用时间/ms
U-Net	76.37±2.53	2.4
U-Net++	79.43±2.56	2.9
CA-Net	83.86±2.74	2.1

Epochs	Batch Size
Epochs	2	4	8	16
100	67.32±2.41	65.24±2.93	62.24±3.35	59.89±3.12
150	73.18±3.06	72.57±3.12	71.34±3.10	71.53±3.27
200	78.23±2.71	78.12±2.61	75.67±2.66	72.45±2.36
250	82.35±2.48	82.23±2.32	81.49±2.88	80.92±3.27
300	83.83±1.87	83.86±2.74	83.51±2.66	81.23±3.07
350	83.71±2.03	83.79±2.85	82.68±2.44	81.36±3.12

形态学后处理方法	Dice系数/%
不做后处理	83.86±2.74
闭运算	84.03±2.81
开运算	83.92±2.54

A Method of Facial Mask Segmentation Based on CA-Net

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