{"title":"基于循环生成对抗网络算法的迷彩服图案设计","authors":"Min Li, Miao Yu, Bingqing Liu, Qinglong Peng","doi":"10.1177/24723444231185633","DOIUrl":null,"url":null,"abstract":"Camouflage refers to an essential means of protection for military reconnaissance. However, the traditional method of camouflage image generation does not allow for end-to-end generation. The algorithm of Cycle Generative Adversarial Network adopted in this article can not only keep the features of original pictures but also realize the end-to-end generation, which can better solve seasonal problems better. The generation model and the discrimination model are trained using the concept of the cyclic confrontation game of Cycle GAN. In the training process, the loss function served to stimulate the background image and camouflage images mapping to each other. The generated image is captured into the recognition model for recognition, so as to provide feedback on the findings. Finally, the camouflage image with background image characteristics is output to realize the generation of an end-to-end camouflage image. The camouflage evaluation index is used to detect the quality of color, texture, and edge of the experimental output image. The generated image shows a good camouflage effect in the color, texture, and comparison of edges, thus verifying the effectiveness of the practical scheme.","PeriodicalId":6955,"journal":{"name":"AATCC Journal of Research","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Pattern Design of A Camouflage Suit Based on the Cycle Generative Adversarial Network Algorithm\",\"authors\":\"Min Li, Miao Yu, Bingqing Liu, Qinglong Peng\",\"doi\":\"10.1177/24723444231185633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Camouflage refers to an essential means of protection for military reconnaissance. However, the traditional method of camouflage image generation does not allow for end-to-end generation. The algorithm of Cycle Generative Adversarial Network adopted in this article can not only keep the features of original pictures but also realize the end-to-end generation, which can better solve seasonal problems better. The generation model and the discrimination model are trained using the concept of the cyclic confrontation game of Cycle GAN. In the training process, the loss function served to stimulate the background image and camouflage images mapping to each other. The generated image is captured into the recognition model for recognition, so as to provide feedback on the findings. Finally, the camouflage image with background image characteristics is output to realize the generation of an end-to-end camouflage image. The camouflage evaluation index is used to detect the quality of color, texture, and edge of the experimental output image. The generated image shows a good camouflage effect in the color, texture, and comparison of edges, thus verifying the effectiveness of the practical scheme.\",\"PeriodicalId\":6955,\"journal\":{\"name\":\"AATCC Journal of Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AATCC Journal of Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/24723444231185633\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, TEXTILES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AATCC Journal of Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/24723444231185633","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, TEXTILES","Score":null,"Total":0}
The Pattern Design of A Camouflage Suit Based on the Cycle Generative Adversarial Network Algorithm
Camouflage refers to an essential means of protection for military reconnaissance. However, the traditional method of camouflage image generation does not allow for end-to-end generation. The algorithm of Cycle Generative Adversarial Network adopted in this article can not only keep the features of original pictures but also realize the end-to-end generation, which can better solve seasonal problems better. The generation model and the discrimination model are trained using the concept of the cyclic confrontation game of Cycle GAN. In the training process, the loss function served to stimulate the background image and camouflage images mapping to each other. The generated image is captured into the recognition model for recognition, so as to provide feedback on the findings. Finally, the camouflage image with background image characteristics is output to realize the generation of an end-to-end camouflage image. The camouflage evaluation index is used to detect the quality of color, texture, and edge of the experimental output image. The generated image shows a good camouflage effect in the color, texture, and comparison of edges, thus verifying the effectiveness of the practical scheme.
期刊介绍:
AATCC Journal of Research. This textile research journal has a broad scope: from advanced materials, fibers, and textile and polymer chemistry, to color science, apparel design, and sustainability.
Now indexed by Science Citation Index Extended (SCIE) and discoverable in the Clarivate Analytics Web of Science Core Collection! The Journal’s impact factor is available in Journal Citation Reports.