A SAR Image Building Detection Algorithm Based on Improved YOLOv3

doi:10.3969/j.issn.1000-1093.2020.07.012

Abstract

Abstract: Since the traditional synthetic aperture radar （SAR）image building detection algorithm is mainly to detect the specific buildings by manually extracting the features in specific scenarios, it always has low average detection accuracy and low detection efficiency. A SAR image building detection algorithm based on improved YOLOv3 is proposed to realize the automatic detection of buildings through deep learning. The SAR image building dataset is produced, and the sizes of priori anchors are re-set by the improved K-means clustering algorithm according to the size characteristics of the buildings. Then the structure of the aggregated residual transformations for deep neural networks is used to construct the feature layer of YOLOv3 skeleton network. The single-channel convolution residual module is improved into a multi-channel convolution residual module to increase the channel information utilization and reduce the computation load. And a shallow feature fusion module is added to increase the contour shape characteristics of buildings in the feature map. The feature layer is upsampled to add detail features before the feature fusion layer through the transposed convolution. The improved YOLOv3 algorithm is used to train the building detection model which is tested on the test dataset. Experimental results show that the improved YOLOv3 algorithm improves the average detection accuracy on SAR image building dataset by 9.2% and the recall rate by 6.3% compared with the original YOLOv3 algorithm, while maintaining a fast detection speed. Key

Key words: buildingdetection, syntheticapertureradarimage, YOLOv3, residualmodule, shallowfeaturefusion

CLC Number:

TP751.1

LI Xiang， SU Juan， YANG Long. A SAR Image Building Detection Algorithm Based on Improved YOLOv3[J]. Acta Armamentarii, 2020, 41(7): 1347-1359.

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第41卷第7期2020 年7月
兵工学报ACTA ARMAMENTARII
Vol.41No.7Jul.2020

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