基于优化区域卷积神经网络的机场区域检测

机场区域因为其特殊性对民用和军用都具有重大意义。基于机器自主识别的机场区域检测方法是目前主流的检测方法,针对传统检测算法对机场区域遥感图像中多类别、多尺度、多视角以及复杂背景下检测鲁棒性不足的问题,本文提出了一种优化的区域卷积神经网络检测算法。首先,构建了一个相比传统数据集包含更多尺度、视角、类别和复杂背景等条件下的机场区域7类典型目标数据集并进行了优化处理,为模型算法的监督训练和调节奠定了基础;然后,根据所检测目标的特性以及网络的局限性,使用差异值法生成anchor、复杂负样本筛选以及加入先验判决网络对原网络进行了优化和仿真验证;最后,对优化的网络模型进行了测试与对比分析。实验结果表明,本文算法在仅增加极少检测时间基础上相比原算法有更高的平均精确度,且对各类目标的检测达到了较好的效果。

Airport Area Detection Based on Optimized Regional Convolutional Neural Network

The airport area has great significance to both civilian and military use because of its particularity.At the same time,the airport area detection method based on machine self-identification is the current mainstream detection method.Aiming at the problem of insufficient robustness of traditional detection algorithms to the detection of multiple categories,multiple scales,multiple perspectives,and complex backgrounds in airport remote sensing images,an improved regional convolutional neural network detection algorithm is proposed.Firstly,compared with the traditional data set,a typical target data set of 7 types of airport areas under more conditions such as scales,perspectives,categories,and complex backgrounds is constructed and optimized,which lays a foundation for the supervised training and adjustment of model algorithms.Then,according to the characteristics of the detected target and the limitations of the network,the difference value method is used to generate the anchor,the complex negative sample screening,and the prior decision network are added to optimize and simulate the original network.Finally,the optimized network model is tested and compared.Experimental results show that the proposed algorithm has higher average accuracy than the original algorithm on the basis of increasing only a small amount of detection time,and achieves better results for various types of targets.