Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network
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摘要: 车辆检测是遥感图像分析领域的热点研究内容之一，车辆目标的智能提取和识别，对于交通管理、城市建设有重要意义。在遥感领域中，现有基于卷积神经网络的车辆检测方法存在实现过程复杂并且对于车辆密集区域检测效果不理想的缺陷。针对上述问题，该文提出基于端到端的神经网络模型DF-RCNN以提高车辆密集区域的检测精度。首先，在特征提取阶段，DF-RCNN模型将深浅层特征图的分辨率统一并融合；其次，DF-RCNN模型结合可变形卷积和可变形感兴趣区池化模块，通过加入少量的参数和计算量以学习目标的几何形变。实验结果表明，该文提出的模型针对密集区域的车辆目标具有较好的检测性能。Abstract: Vehicle detection is one of the hotspots in the field of remote sensing image analysis. The intelligent extraction and identification of vehicles are of great significance to traffic management and urban construction. In remote sensing field, the existing methods of vehicle detection based on Convolution Neural Network (CNN) are complicated and most of these methods have poor performance for dense areas. To solve above problems, an end-to-end neural network model named DF-RCNN is presented to solve the detecting difficulty in dense areas. Firstly, the model unifies the resolution of the deep and shallow feature maps and combines them. After that, the deformable convolution and RoI pooling are used to study the geometrical deformation of the target by adding a small number of parameters and calculations. Experimental results show that the proposed model has good detection performance for vehicle targets in dense areas.
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表 1 3 种尺度和 3 种比例映射候选区域尺寸
区域尺寸和比例 202, 1:1 202, 1:2 202, 2:1 302, 1:1 302, 1:2 302, 2:1 402, 1:1 402, 1:2 402, 2:1 映射区域大小 20×20 14×28 28×14 30×30 21×42 42×21 40×40 28×56 57×28 
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表 2 不同层检测性能比较
模型 检测区域 召回率 准确率 F1指标 层1 密集区域 0.461 0.611 0.525 层3 密集区域 0.623 0.754 0.682 层5 密集区域 0.655 0.923 0.766 层3+5 密集区域 0.714 0.930 0.808 层2+4 密集区域 0.709 0.925 0.803 层1+3+5 密集区域 0.725 0.927 0.814
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表 3 结合可变形模块检测性能比较
模型 检测区域 召回率 准确率 F1指标 层1+3+5，可变形卷积 密集区域 0.731 0.932 0.819 层1+3+5，可变形RoI池化 密集区域 0.726 0.925 0.814 层1+3+5，可变形卷积，可变形RoI池化 密集区域 0.744 0.940 0.831 层5，可变形卷积，可变形RoI池化 密集区域 0.725 0.924 0.812 层2+4，可变形卷积，可变形RoI池化 密集区域 0.715 0.922 0.805 层3+5，可变形卷积，可变形RoI池化 密集区域 0.729 0.928 0.817
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表 4 不同模型检测性能比较
模型 检测区域 召回率 准确率 F1指标 Faster-RCNN 密集区域 0.655 0.932 0.766 DCNN 密集区域 0.402 0.925 0.560 DF-RCNN 密集区域 0.744 0.940 0.831 Faster-RCNN 非密集区域 0.901 0.942 0.921 DCNN 非密集区域 0.962 0.785 0.865 DF-RCNN 非密集区域 0.910 0.952 0.931
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