基于卷积神经网络的小型建筑物检测算法

doi:10.12305/j.issn.1001-506X.2021.11.08

Abstract

Abstract:

Aiming at the low accuracy of building target detection algorithm based on traditional convolutional neural network for small buildings, a small target detection algorithm model based on Mask-region convolutional neural networks (Mask-RCNN) model is proposed. The model improves the feature extraction network in Mask-RCNN model, and designs a multi-scale group convolution neural network with attention mechanism, which effectively solves the problem that small targets have few useful features and are easy to be disturbed by background features and noise. The experimental results of aerial images show that the improved detection model improves the target detection accuracy of small buildings by 28.9% compared with the original Mask-RCNN model, reaching 0.663. And the overall detection accuracy reaches 0.843, which effectively improves the accuracy of aerial building detection.

Key words: building detection, small target detection, convolutional neural network(CNN), feature extraction, attention mechanism

CLC Number:

TP391

Ruochen ZHAO, Jingdong WANG, Siyu LIN, Dongze GU. Small building detection algorithm based on convolutional neural network[J]. Systems Engineering and Electronics, 2021, 43(11): 3098-3106.

Figures/Tables 16

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References 30

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网络层	Resnet101	Densenet169
C1	7×7, 2k 3×3, max pool	7×7, 2k 3×3, MaxPooling
C2	$\left[ {\begin{array}{*{20}{c}}{1 \times 1, 2k}\\{3 \times 3, 2k}\\{1 \times 1, 8k}\end{array}} \right] \times 3 $	$\begin{array}{c}\left[ {\begin{array}{*{20}{c}}{1 \times 1, k}\\{3 \times 3, k}\\{1 \times 1, k}\end{array}} \right] \times 6\\1 \times 1, 0.5k\\2 \times 2\;\;{\rm{AveragePooling}}\end{array}$
C3	$\left[ {\begin{array}{*{20}{c}}{1 \times 1, 4k}\\{3 \times 3, 4k}\\{1 \times 1, 16k}\end{array}} \right] \times 4 $	$\begin{array}{c}\left[ {\begin{array}{*{20}{c}}{1 \times 1, k}\\{3 \times 3, k}\\{1 \times 1, k}\end{array}} \right] \times 12\\1 \times 1, 0.5k\\2 \times 2\;\;{\rm{AveragePooling}}\end{array} $
C4	$\left[ {\begin{array}{*{20}{c}}{1 \times 1, 8k}\\{3 \times 3, 8k}\\{1 \times 1, 32k}\end{array}} \right] \times 23 $	$ \begin{array}{c}\left[ {\begin{array}{*{20}{c}}{1 \times 1, k}\\{3 \times 3, k}\\{1 \times 1, k}\end{array}} \right] \times 32\\1 \times 1, 0.5k\\2 \times 2\;\;{\rm{AveragePooling}}\end{array}$
C5	$\left[ {\begin{array}{*{20}{c}}{1 \times 1, 16k}\\{3 \times 3, 16k}\\{1 \times 1, 64k}\end{array}} \right] \times 3 $	$ \begin{array}{c}\left[ {\begin{array}{*{20}{c}}{1 \times 1, k}\\{3 \times 3, k}\\{1 \times 1, k}\end{array}} \right] \times 32\\1 \times 1, 0.5k\\2 \times 2\;\;{\rm{AveragePooling}}\end{array}$

建筑物数量	训练集	测试集
小型建筑物/个	2 652	156
中型建筑物/个	3 736	186
大型建筑物/个	2 600	137
总数/个	8 988	479

方法	AP	AP_s	AP_m	AP_l
Resnet 101	0.739	0.514	0.849	0.847
Densenet 169	0.781	0.563	0.882	0.892
Densenet 169 +多尺度组卷积	0.815	0.652	0.891	0.899
Densenet 169+多尺度组卷积+SKnet	0.843	0.663	0.929	0.934

网络	参数
网络	FPS	FLOPs	AP
VGG16	1.93	15.5G	0.554
Resnet101	1.86	7.85G	0.739
InceptionV4	1.98	2.85G	0.755
Resnext101	1.67	8.03G	0.780
Densenet169	1.65	3.42G	0.781
本文网络	1.64	3.47G	0.843

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Small building detection algorithm based on convolutional neural network

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