Military Cockpit Color Design Method Based on Deep Representation Learning and Genetic Algorithm

doi:10.12382/bgxb.2023.0456

Abstract

Abstract:

The color design of military cockpits is a subjective part of industrial design for manned military equipment, and the rationality of the design is crucial. A military cockpit color matching method based on deep representation learning and genetic algorithm is proposed to improve the scientific nature of military cockpit color design. This method uses a deep representation learning model to predict the military cockpit color schemes and establishes a military cockpit color matching model based on color perception theory, which serves as a constraint condition for generating schemes. At the same time, an interactive genetic algorithm is introduced into the intelligent color matching system to optimize the parameters of neural network through manual guidance, and effectively iterate the predicted color matching schemes. The results show that the color matching schemes generated by the proposed method comply with the military cockpit color matching model, and the prediction accuracy of the proposed model combined with genetic algorithm is improved by 16%~18% compared to a single deep representation model. Compared to manual color design schemes, the satisfaction of the color schemes generated by the military cockpit intelligent color matching method is slightly higher, the design cycle is shortened by 80%~88%, and the color stability is improved by 6%~12%.

Key words: military cockpit color matching, deep representation learning, interactive genetic algorithm, color perception

CLC Number:

TP183

SU Sheng, GU Sen, SONG Zhiqiang, LIU Ping. Military Cockpit Color Design Method Based on Deep Representation Learning and Genetic Algorithm[J]. Acta Armamentarii, 2024, 45(4): 1060-1069.

Figures/Tables 11

References 35

[1]	TAO W Y, WU Y, LI W F, et al. Influence of classroom colour environment on college students' emotions during campus lockdown in the COVID-19 post-pandemic era—a case study in Harbin, China[J]. Buildings, 2022, 12(11): 1873.
[2]	李中付, 余浩, 时粉周, 等. 船舶舱室色彩搭配对船员工效影响研究[J]. 船舶工程, 2014, 36(1):82-86.
	LI Z F, YU H, SHI F Z, et al. Researches on experimentation with influence of cabin color on crew's performance[J]. Ship Engineering, 2014, 36(1):87-91. (in Chinese)
[3]	黄璞. 色彩美学在船舶舱室设计中的应用[J]. 包装工程, 2022, 43(12):273-277 HUANG P.
	Application of color aesthetics in ship cabin design[J]. Packaging Engineering, 2022, 43(12):273-277. (in Chinese)
[4]	魏蓉, 宋志强, 苏胜. 基于色彩模型的车载指挥方舱设计[J]. 包装工程, 2020, 41(6):86-93.
	WEI R, SONG Z Q, SU S. Design of vehicle-mounted commend cabin based on color model[J]. Packaging Engineering, 2020, 41(6): 86-93. (in Chinese)
[5]	李中付, 丁猛, 余浩, 等. 基于色彩心理与工效的船舱室色彩搭配原则[J]. 船舶工程, 2015, 37(增刊1):203-205, 260.
	LI Z F, DING M, YU H, et al. Fundamental requirements of cabin color design with psychology of color vision and ergonomics[J]. Ship Engineering, 2015, 37(S1): 203-205, 260. (in Chinese)
[6]	朱昱宁, 徐博群, 刘肖健. 基于交互式遗传算法的参考图像辅助配色设计[J]. 包装工程, 2020, 41(2):181-188.
	ZHU Y N, XU B Q, LIU X J. Reference image aided color matching design based on interactive genetic algorithm[J]. Packaging Engineering, 2020, 41(2): 181-188. (in Chinese)
[7]	DING M, DONG W. Multiemotional product color design using gray theory and nondominated sorting genetic algorithm-Ⅲ[J]. Color Research and Application, 2020, 45(1): 142-155.
[8]	刘肖健, 曹愉静, 赵露唏. 传统纹样的色彩网络模型及配色设计辅助技术[J]. 计算机集成制造系统, 2016, 22(4): 897-905.
	LIU X J, CAO Y J, ZHAO L X. Color networks of traditional cultural patterns and color design aiding technology[J]. Computer Integrated Manufacturing Systems, 2016, 22(4): 897-905. (in Chinese)
[9]	刘肖健, 贾莹莹, 徐博群. 配色设计研究现状综述[J]. 包装工程, 2021, 42(14):92-100.
	LIU X J, JIA Y Y, XU B Q. Review on the study of color design[J]. Packaging Engineering, 2021, 42(14): 92-100. (in Chinese)
[10]	BITTERMANN M S. Insight into color aesthetics from probabilistic perception modeling[J]. Color Research & Application, 2018, 43(4): 527-543.
[11]	HU G S, LIU Z, WANG Y, et al. Calculative modeling for quantified semantics‐color mapping[J]. Color Research and Application, 2020, 45(3): 465-476.
[12]	DING M, BAI Z H. Product color emotional design adaptive to product shape feature variation[J]. Color Research & Application, 2019, 44(5): 811-823.
[13]	苏胜, 宋志强, 闫敏, 等. 军用座舱赋色原则研究[J]. 西安工业大学学报, 2022, 42(3):317-323.
	SU S, SONG Z Q, YAN M, et al. Research on and application of factor extraction model for color design of military cabin[J]. Journal of Xi'an Technological University, 2022, 42(3):317-323. (in Chinese)
[14]	苏胜, 宋志强, 顾森, 等. 军用舱室色彩感知下配色方法研究[J]. 西安工业大学学报, 2022, 42(1):96-104.
	SU S, SONG Z Q, GU S, et al. Color matching method based on color perception for military cabins[J]. Journal of Xi'an Technological University, 2022, 42(1): 96-104. (in Chinese)
[15]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[16]	GUO Y M, LIU Y, OERLEMANS A, et al. Deep learning for visual understanding: a review[J]. Neurocomputing, 2016, 187: 27-48.
[17]	HAO X, ZHANG G G, MA S. Deep learning[J]. International Journal of Semantic Computing, 2016, 10(3): 417-439.
[18]	张凤, 张超, 杨华民, 等. 作战辅助系统中基于深度学习的投影图像颜色补偿方法[J]. 兵工学报, 2021, 42(11):2418-2423. doi: 10.3969/j.issn.1000-1093.2021.11.015
	ZHANG F, ZHANG C, YANG H M, et al. Deep learning-based color compensation method for projected images in combat assistant system[J]. Acta Armamentarii, 2021, 42(11): 2418-2423. (in Chinese) doi: 10.3969/j.issn.1000-1093.2021.11.015
[19]	BAU D, LIU S, WANG T Z, et al. Rewriting a deep generative model[C]// Proceedings of the 16th European Conference on Computer Vision. Glasgow, UK: Springer, 2020: 351-369.
[20]	WARNES A. Saudi international airshow[J]. Airforces Monthly, 2019(374):30-35.
[21]	LIU F. CASC in Zhuhai airshow 2012[J]. Aerospace China, 2012, 13(4):2-5.
[22]	CHO S B. Towards creative evolutionary systems with interactive genetic algorithm[J]. Applied Intelligence, 2002, 16(2):129-138.
[23]	LEE J H, KIM H S, CHO S B. Accelerating evolution by direct manipulation for interactive fashion design[C]// Proceedings of the 4th International Conference on Computational Intelligence and Multimedia Applications. Yokusika, Japan: IEEE, 2001.
[24]	DAWKINS R. The evolution of evolvability[M]//Langton C. Artificial life. New York, NY, US: Routledge, 2019: 201-220.
[25]	JAKSA R, TAKAGI H. Tuning of image parameters by interactive evolutionary computation[C]// Proceedings of 2003 IEEE International Conference on Systems, Man and Cybernetics. Washington, DC, US: IEEE, 2003.
[26]	BAIK S, BALA J, HADJARIAN A, et al. Visualization for genetic evolution of target movement in battle fields[C]// Proceedings of the Computational Science - {ICCS} 2005. Atlanta, GA, US: Springer-Verlag, 2005.
[27]	BANDTE O, MALINCHIK S. A broad and narrow approach to interactive evolutionary design - an aircraft design example[C]// Proceedings of the Genetic and Evolutionary Computation - GECCO 2004. Seattle, WA, US: Springer-Verlag, 2004.
[28]	TAKAGI H. Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation[J]. Proceedings of the IEEE, 2001, 89(9):1275-1296.
[29]	KIM H S, CHO S B. Application of interactive genetic algorithm to fashion design[J]. Engineering Applications of Artificial Intelligence, 2000, 13(6):635-644.
[30]	CONGDON C B, MAZZA R H. GenTree: an interactive genetic algorithms system for designing 3D polygonal tree models[C]// Proceedings of the Genetic and Evolutionary Computation - GECCO 2003. Chicago, IL, US: Springer-Verlag, 2003.
[31]	FAROOQ H, ZAKARIA N, SIDDIQUE M T. An interactive visualization of genetic algorithm on 2-D graph[J]. International Journal of Software Science & Computational Intelligence, 2012, 4(1):34-54.
[32]	REN J, GONG D W, SUN X Y, et al. Interactive genetic algorithms with variational population size[C]// Proceedings of the 5th International Conference on Intelligent Computing. Ulsan, South Korea: Springer-Verlag, 2009.
[33]	DENG W, ZHANG X, ZHOU Y, et al. An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems[J]. Information Sciences, 2022, 585: 441-453.
[34]	JALALI Z, NOORZAI E, HEIDARI S. Design and optimization of form and facade of an office building using the genetic algorithm[J]. Science and Technology for the Built Environment, 2020, 26(2): 128-140.
[35]	GIASSI M, GÖTEMAN M. Layout design of wave energy parks by a genetic algorithm[J]. Ocean Engineering, 2018, 154: 252-261.

色彩种类	色彩取值			色彩意象
色彩种类	H	S	B	色彩意象
皮肤色	0~280	0~91	5~88	深沉,安全,现代,理性
填充物色	16~350	2~86	25~100	和谐,舒适,大气,清爽
特殊色	18~357	3~87	2~87	人性,柔和,解压,轻快

色彩种类	色彩取值			色彩意象
色彩种类	H	S	B	色彩意象
皮肤色	0~280	0~91	5~88	深沉,安全,现代,理性
填充物色	16~350	2~86	25~100	和谐,舒适,大气,清爽
特殊色	18~357	3~87	2~87	人性,柔和,解压,轻快

算法关键参数	数值
迭代步数	50
学习率	0.01
潜层维度	256
随机失活率	0.5

算法关键参数	数值
迭代步数	50
学习率	0.01
潜层维度	256
随机失活率	0.5

方法	座舱型号
方法	S*5型	C/*5型	7*A型
设计师配色	32	32	32
基于深度表征学习和遗传算法的军用座舱色彩设计方法	5.1	3.8	6.5