融合深层特征与边缘特征的皮肤检测校正算法  

Skin Detection and Correction Algorithm Combining Deep Feature and Edge Feature

作　　者：王芸 WANG Yun(College of Computer Science and Technology,China University of Petroleum,Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院,青岛266580

出　　处：《计算机系统应用》2022年第8期298-304,共7页Computer Systems & Applications

基　　金：中石油重大科技项目(ZD2019-183-007);山东省自然科学基金(ZR2020MF140)。

摘　　要：当皮肤区域与非皮肤区域没有明显边界时,皮肤检测变得更加困难.针对这一问题,本文提出了一种新的皮肤检测校正算法.本文首先利用卷积神经网络分级对皮肤的颜色、纹理等特征进行提取,通过门控卷积层对皮肤与非皮肤像素的边界区域进行细化,以增强皮肤检测的效果,最后利用ASPP将深层信息与边缘信息进行融合.本文将经过阈值粗分割的检测结果作为输入,在ECU和Pratheepan两个数据集上进行了评估,实验结果表明,本算法在ECU数据集上的准确率达到了91%,在Pratheepan数据集的准确率达到了95%,与现有方法相比,本文算法的性能有明显的提升.When no obvious boundaries exist between skin regions and non-skin regions,skin detection becomes extremely difficult.To solve this problem,we propose a new skin detection and correction algorithm.Firstly,this study uses a convolutional neural network(CNN)to extract skin features such as colors and texture step by step and then subdivides the boundary region of skin and non-skin pixels through the gated convolutional layer to enhance the effect of skin detection.Finally,ASPP is applied to fuse deep information and edge information.The detection results from rough threshold segmentation are used as input for the evaluation on ECU and Pratheepan datasets.The experimental results show that the accuracy of this algorithm reaches up to 91%on the ECU dataset and 95%on the Pratheepan dataset.The performance of the proposed algorithm has been significantly improved compared with that of the existing methods.

关 键 词：皮肤检测 校正算法 深层特征 边缘细化 特征识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R318[自动化与计算机技术—计算机科学与技术]