Journal of Textile Research ›› 2024, Vol. 45 ›› Issue (05): 94-101.doi: 10.13475/j.fzxb.20230201501

• Dyeing and Finishing Engineering • Previous Articles     Next Articles

Fully automated camouflage pattern design based on background stitching and texture templates

ZHAN Yuting1, MEI Chennan1, WANG Yan2, XIAO Hong2, ZHONG Yueqi1,3()   

  1. 1. College of Textiles, Donghua University, Shanghai 201620, China
    2. Institute of Systems Engineering, Academy of Military Sciences, Beijing 100010, China
    3. Key Laboratory of Textile Science & Technology, Ministry of Education, Donghua University, Shanghai 201620, China
  • Received:2023-02-08 Revised:2023-09-19 Online:2024-05-15 Published:2024-05-31

Abstract:

Objective In the military, the ability to reponse quickly to the battlefield environment can determine the success or failure of a battle. Therefore, It is need ed to improve the design speed of camouflage patterns and the ability to adapt to the target environment to enhance the camouflage effect of patterns and thus improve the military level of the army.

Method First, we automatically combined the background environment dataset into a stitching map and extracted the primary color of the background stitching map by mean clustering method; then, we proposed four automatic design methods for camouflage texture templates, including multi-circular random distribution method, WGN Fourier spectrum method, texture image generation method, and layered cloud method. Finally, after the mean clustering of camouflage texture templates, the camouflage pattern was obtained by replacing the template colors with the primary colors.

Results Experiments were conducted to construct virtual scenes and a dataset of target background images was created. We stitched the images in the dataset into a single large stitched image. The stitched image was detected based on a color histogram in RGB color space, and a cluster K value of 6 was determined. Six colors were extracted from the eight virtual jungle terrain scenes as primary colors, and four different camouflage patterns were generated based on a texture template method. The experimental time was short, and the design rate was high. In order to reduce the impact of environmental and human factors, camouflage assessment has been relatively carried out using templates based on multi-circular random distribution method, templates based on WGN Fourier spectrum method, templates based on texture image generation method, and templates based on layered cloud method as camouflage targets, and non-environment specific jungle camouflage (FLECKTARN-style jungle camouflage) as a reference. In the subjective evaluation experiments, the experimental results. Each test set had a 100% probability of discovery and different average search times, with the test set of camouflage patterns designed based on the multi-circular random distribution method having the longest average search time of 1.561 4 s. The average search times of all four camouflage patterns designed using this paper's method were greater than those of the reference pattern. Among them, the camouflage search time increased by 8.1% for the template based on the multi-circular random distribution method, 1.1% for the template based on the WGN Fourier spectroscopy method, 2.4% for the template based on the texture image generation method, and 3.7% for the template based on the layered cloud coloring method. In the objective evaluation experiments, the experimental results were shown. The average search time of the PF-Net network for the test set images was the same for both, 0.04 s, but the probability of discovery was different. The detection probability of the test sets designed according to the fully automated camouflage pattern design method based on background stitching and texture templates were both lower than that of the reference target, with the detection probability of the template camouflage based on the multi-circular random distribution method being 45% lower than the detection probability of the reference target.

Conclusion For automatic camouflage to achieve excellent results, it is necessary to react quickly to generate camouflage colors and textures according to changes in the target environment. This paper proposes a fully automatic camouflage pattern design method based on background splicing and texture templates with a high design rate, rapid response to the target environment, and good camouflage effect according to the modern army's demand for camouflage combined with computer technology. Through the subjective and objective evaluation of camouflage detection, the camouflage effect of the camouflage pattern is evaluated with the search time and detection probability as the primary indexes. The feasibility and effectiveness of the method are verified, and the generated pattern has a good camouflage effect. The camouflage design method in this paper provides various ideas for constructing camouflage design and achieves a more accurate and convenient camouflage pattern design for target area environments.

Key words: camouflage, pattern design, mean clustering, texture template, camouflage detection

CLC Number: 

  • TS941.2

Fig.1

Camouflage design process"

Fig.2

Multi-circular random distribution method process"

Fig.3

WGN Fourier spectrum process"

Fig.4

Texture image generation method process"

Fig.5

Layered cloud method process"

Fig.6

Example of target background image"

Fig.7

Similarity of color histogram with original image at different k values"

Fig.8

Camouflage patterns automatically generated by four ways"

Fig.9

Jungle camouflage in non-target environments"

Fig.10

Soldier models. (a) MC-RD; (b) WGN-FFT; (c) TIG; (d) LC; (e)Jungle camouflage in non-target environments"

Fig.11

Example of test set image"

Fig.12

Example of interference image"

Tab.1

Test set images subjective evaluation"

实验测试集图像 发现概率/% 平均搜索时间/s
MC-RD 100 1.561 4
WGN-FFT 100 1.460 4
TIG 100 1.478 2
LC 100 1.497 3
非针对目标环境的丛林迷彩 100 1.443 5

Fig.13

Example of significant result graph. (a) MC-RD; (b) WGN-FFT; (c) TIG; (d) LC; (e)Jungle camouflage in non-target environments"

Tab.2

Test set images PF-Net objective evaluation"

实验测试
集图像
发现概率/% 平均搜索
时间/s
MC-RD 40 0.04
WGN-FFT 45 0.04
TIG 50 0.04
LC 55 0.04
非针对目标环境的丛林迷彩 85 0.04
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