光学学报, 2019, 39 (12): 1228002, 网络出版: 2019-12-06   

基于二值语义分割网络的遥感建筑物检测 下载: 981次

Remote Sensing Building Detection Based on Binarized Semantic Segmentation
朱天佑 1,2,3董峰 1,2龚惠兴 1,2,*
作者单位
1 中国科学院红外探测与成像技术重点实验室, 上海 200083
2 中国科学院上海技术物理研究所, 上海 200083
3 中国科学院大学, 北京 100049
摘要
针对遥感建筑物实时检测中深度卷积网络资源消耗大和硬件移植难的问题,提出一种基于二值与浮点数混用方法的语义分割网络MBU-Net。通过对FU-Net网络全局权重进行二值化处理来压缩模型大小,并将占少量参数的网络输出层权重替换成浮点型(MBU-Net),解决了全局二值网络(GBU-Net)检测精度差、训练缓慢的问题。在QuickBird卫星遥感数据集上进行实验,MBU-Net的像素准确率为82.33%,F1分数(召回率和精确率的调和平均数)为73.15%;相比于FU-Net,MBU-Net在保证检测精度的前提下,模型大小大幅压缩,检测速度提升了6.29倍,功耗降为37.78%,且优于其他同类方法(Deeplab、ENet),对遥感建筑物实时检测具有重要的实际工程价值。
Abstract
To address the problem of high resource consumption and difficulty of hardware transplantation involved in utilizing deep convolutional networks for real-time detection of remote sensing building, a semantic segmentation network based on the mixed method of binary and floating-point parameters, i.e., mixed binary U-shape network (MBU-Net), is proposed. To compress the model size, the weights of a float U-shape network (FU-Net) are binarized. The output layer weights that account for a small number of parameters are replaced by floating-point type parameters to resolve the poor detection accuracy and low training speed in a global binary network. Experiments using the QuickBird satellite remote sensing dataset show that the pixel accuracy of MBU-Net is 82.33% and the harmonic average of the recall rate and accuracy rate (F1 score ) is 73.15%. Compared with the FU-Net,the MBU-Net can ensure the detection accuracy. The size of model is greatly compressed, the detection speed is increased by 6.29 times, and the power consumption is reduced to 37.78%, further demonstrating that the MBU-Net is superior to other similar methods (Deeplab and ENet). This finding has important practical engineering value for the real-time detection of remote sensing buildings.

朱天佑, 董峰, 龚惠兴. 基于二值语义分割网络的遥感建筑物检测[J]. 光学学报, 2019, 39(12): 1228002. Tianyou Zhu, Feng Dong, Huixing Gong. Remote Sensing Building Detection Based on Binarized Semantic Segmentation[J]. Acta Optica Sinica, 2019, 39(12): 1228002.

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