结合视觉特征和深度神经网络的面部迷彩伪装效果评估方法

A Study on a Simulation Evaluation Method for Facial Camouflage Effect

针对传统迷彩伪装评估中存在的主观性强和指标单一问题,本文提出了一种结合视觉特征与深度神经网络(DNN)的面部迷彩综合评估方法。该方法融合边缘检测、相似度指标及DNN模块的人脸检测,形成一种全面的评价体系。通过在四种自然背景下的仿真实验验证,本研究可以有效量化面部迷彩在不同背景下的伪装效果,并为未来面部迷彩设计和实施提供有效指导,对提高面部迷彩的实用性和伪装效果具有积极作用。

Addressing the prevalent issues of subjectivity and the limited scope of criteria in traditional camouflage assessment,this paper introduces an integrated evaluation method for facial camouflage that combines visual features with Deep Neural Networks(DNN).This method integrates edge detection,similarity metrics,and DNN-based facial recognition to establish a comprehensive evaluation system.Validated through simulation experiments in four natural environments,this study effectively quantifies the effectiveness of facial camouflage in various backgrounds.It offers valuable guidance for future design and implementation of facial camouflage,positively influencing its practicality and effectiveness.