Garment image instance segmentation method based on improved YOLACT
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％.
garment image instance segmentationYOLACTdepth separable convolutioneffi-cient channel attentionactivation function
国家科技期刊平台
NSTL
万方数据
