Remote sensing image aircraft detection supported by deep convolutional neural network

doi:10.13474/j.cnki.11-2246.2019.0177

Abstract

Abstract: Aiming at the problem that YOLOv3 (You look only once) algorithm has poor detection of small targets and many missed detection, this paper proposes an optimized YOLOv3 algorithm. Firstly, K-means algorithm is used to calculate the anchor frame suitable for the data set in this paper. Then, extended convolution is introduced into YOLOv3 network to enhance the high-level perception field of the network, improve the detection effect of small targets, and secondly, depthwise separable convolution is used. It replaces the ordinary convolution in YOLOv3 network residual module, reduces the calculation parameters, and thus obtains a new convolution neural network structure. Then the comparative experiments are carried out on the data set in this paper. The experimental results show that the optimized YOLOv3 algorithm can detect more targets and reduce the missed detection rate. Compared with YOLOv3 algorithm, its recall rate is increased by 11.86% and F1-score by 2.99%.

Key words: YOLOv3, remote sensing image, target detection, dilated convolution, depthwise separable convolutions

CLC Number:

P237

XIE Meng, LIU Wei, YANG Mengyuan, CHAI Qi, JI Li. Remote sensing image aircraft detection supported by deep convolutional neural network[J]. 测绘通报, 2019, 0(6): 19-23.

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