基于机器学习的迷彩伪装效果评价方法

针对迷彩伪装效果评价算法中评价指标权重分配复杂性,算法开发平台灵活性的问题,提出了基于多特征指标决策树的评价方法。该方法依据视觉注意力机制选择纹理、颜色、亮度、结构相似度与伪装目标尺寸这5项特征作为评价指标,使用机器学习决策树分类器训练出迷彩伪装效果评价模型,将模型移植入体积小、功耗低的树莓派开发平台上。通过与均值权重法、熵权法两种评价方法进行准确率对比实验,其中均值权重法准确率为56；熵权法准确率为74；该方法准确率为90。通过实时性实验证明该方法可以在场外2s左右得到迷彩伪装效果评价结果。

Camouflage effect evaluation method based on machine learning

In this paper,a new evaluation method based on a multi feature indicator decision tree is proposed to address the complexity in weight allocation for evaluation metrics and the flexibility of algorithm development platforms in camouflage effectiveness evaluation.The method selects five features,texture,color,brightness,structural similarity,and camouflage target size,as evaluation indicators based on visual attention mechanisms and trains a camouflage effectiveness evaluation model using a machine learning decision tree classifier,which is ported to a small sized,low power Raspberry Pi development platform.Through the accuracy comparison experiment with two evaluation methods of mean weight method and entropy weight method,the accuracy of mean weight method is 56%,the accuracy rate of entropy weight method is 74%,and the proposed method achieves an accuracy of 90%.The real time experiments demonstrate that the method can get the evaluation results of camouflage effect in about two seconds outside the field.