纺织高校基础科学学报2024，Vol.37Issue(2)：82-91,10.DOI:10.13338/j.issn.1006-8341.2024.02.009
改进YOLACT的服装图像实例分割方法
Garment image instance segmentation method based on improved YOLACT
摘要
Abstract
A garment image instance segmentation method based on improved YOLACT was proposed to solve the problem of low accuracy and speed of clothing image instance segmentation.Based on the YOLACT model,firstly,the depth separable convolution was used in the ResNetl01 network to replace the traditional convolution,reduce the amount of model calculation and model parameters,and accelerate the speed of the model.Then,the efficient channel attention module was introduced to optimize the output features after the protonet,capture the cross-channel inter-action information of the clothing image,and strengthen the feature extraction ability of mask branches.Finally,the Leaky ReLU activation function was used in the training process to ensure that the weight information is updated in time,and to improve the model's ability to extract the negative feature information of the clothing image.The experimental results show that compared with the original model,the proposed method can effectively reduce the number of model param-eters,and increase the accuracy and the speed.The speed increased by 4.82 frame per second,and the average accuracy increased by 5.4％.关键词
服装图像实例分割/YOLACT/深度可分离卷积/高效通道注意力/激活函数Key words
garment image instance segmentation/YOLACT/depth separable convolution/effi-cient channel attention/activation function分类
轻工纺织引用本文复制引用
顾梅花,董晓晓,花玮,崔琳..改进YOLACT的服装图像实例分割方法[J].纺织高校基础科学学报,2024,37(2):82-91,10.基金项目
国家自然科学基金青年基金(61901347) （61901347）
