Cotton Image Segmentation Algorithm Based on Lightweight Symmetric Structure Network
The automation and digital equipment in the cotton production line have improved the production effi-ciency and quality of the production line,but there is still the problem that the cotton condition in the pipe flow of the cotton production line is difficult to detect.Therefore,based on the characteristics of cotton flow images in cotton spin-ning lines,this paper proposes a segmentation method based on lightweight network(Light cotton-net).The network is based on a symmetric encoding and decoding structure.By optimizing the convolution method and upsampling method and designing the special name extraction structure,the network parameters are greatly reduced and the pre-diction speed is improved while the segmentation accuracy is within the acceptable range of error.The data set of cot-ton stream images taken in the fiber machine was used to add random migration,scaling,brightness transformation and other data augmentation operations.Experimental data show that the segmentation accuracy and recall rate of the mod-el are 96.63%and 93.87%respectively when the number of network parameters is 6.0m(million)and the prediction time of each image is 35.328ms.The segmentation accuracy of the model is basically the same as that of the U-NET network,the number of parameters is about 1/3,and the image segmentation speed is about 5 times.The model requires less memory and less computing power and is more suitable for deployment on industrial equipment.
Cotton imageImage segmentation algorithmSymmetric structure networkCotton stream data set
NETL
NSTL
万方数据
维普
