Welcome to Acta Armamentarii ! Today is Share:

Acta Armamentarii ›› 2021, Vol. 42 ›› Issue (9): 1987-1997.doi: 10.3969/j.issn.1000-1093.2021.09.019

• Paper • Previous Articles     Next Articles

Simulation Design of Camouflage Target Concealing Strategy Based on Evolutionary Computation

ZHOU Ying1,2, XIE Zhenping1,2, JIANG Xiaojun3   

  1. (1.School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,Jiangsu,China;2.Jiangsu Key Laboratory of Media Design and Software Technology,Wuxi 214122,Jiangsu,China;3.Science and Technology on Near-Surface Detection Laboratory,Wuxi 214035,Jiangsu,China)
  • Online:2021-10-20

Abstract: For the concealment problem of military equipment in typical natural environment, a novel simulation design method is proposed by combining a modified evolutionary computation strategy and image simulation design technique. The proposed method contains following parts: the fusion simulation computation of camouflage objects and scene images,the camouflage target concealment strategy simulation using optical illusion strips coverage,the search computation based on particle swarm optimization algorithm and probability distribution sampling algorithm,and the calculation of fusion degree between camouflage objects and background scenes based on deep neural network image features. Based on the deep neural network image segmentation model,the recognition rate of camouflage target segmentation is introduced to evaluate the performance of the proposed method. Simulation experimental result shows that the average fusion degree of concealment simulation images obtained in woodland and desert environment can reach above 0.99,and the average segmentation recognition rate of camouflage targets is lower than 0.90. The proposed method can provide very effective results for designing the concealment strategy of a given target in a scene image,and has good practical value and scalability.

Key words: camouflagetarget, concealingstrategy, evolutionarycomputation, probabilitydistributionsampling, deepneuralnetwork

CLC Number: