基于角度敏感的空间注意力机制的轻量型旋转目标检测器

doi:10.11959/j.issn.2096-6652.202133

Abstract

Abstract:

With the rapid development of deep learning, more and more target detection algorithms based on anchor frame are applied to remote sensing images in recent years.However, the cost of improving the accuracy of the algorithm is to sacrifice the detection speed.Therefore, the target detection network framework of anchor free was chosen, and a remote sensing detection algorithm of rotating frame was proposed according to the characteristics of remote sensing scene.A simple and effective representation of rotating frame was proposed according to the spatial position relationship between rotating frame and its external rectangular frame.In addition, an angle sensitive attention mechanism was designed to assist the detection of rotating targets.By introducing angle information, the detection ability of the model for rotating targets was improved.The proposed algorithm was tested on the open remote sensing dataset DOTA.The mean average precision of target detection network of rotating frame is 68.5% and the detection speed is 17.4 frames per second.

Key words: rotation detection, remote sensing image, anchor free, attention mechanism

CLC Number:

TP181

Kaishi YIN, Meng YANG, Xi GU, et al. Light weight rotating object detector based on angle sensitive spatial attention mechanism[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 322-333.

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References 20

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类别	方法1	方法2	方法3	方法4
飞机	93.0%	92.2%	71.8%	57.1%
轮船	80.6%	80.1%	14.5%	51.9%
储罐	89.3%	89.7%	83.9%	60.4%
棒球场	67.7%	54.3%	57.4%	37.7%
网球场	94.6%	94.8%	55.7%	51.8%
篮球场	62.3%	50.7%	46.6%	40.6%
地面跑道	53.7%	44.4%	42.3%	41.5%
港口	55.5%	46.5%	19.3%	34.5%
桥梁	29.7%	24.2%	9.0%	23.5%
大型车辆	78.7%	76.5%	9.3%	43.4%
小型车辆	53.4%	57.1%	17.0%	38.8%
直升机	13.8%	25.7%	41.5%	29.6%
环形交叉路口	69.6%	65.3%	64.3%	47.2%
足球场	62.6%	39.4%	47.9%	38.8%
游泳池	52.2%	49.2%	28.1%	35.1%
mAP	63.7%	59.3%	40.5%	42.1%

类别	Resnet50	Restnet50+atten	Resnet101	Resnet101+atten
飞机	88.6%	88.7%	89.0%	88.8%
轮船	79.0%	82.7%	79.2%	84.0%
储罐	86.2%	86.6%	87.1%	85.0%
棒球场	68.6%	72.6%	73.8%	72.8%
网球场	90.7%	90.4%	90.8%	90.7%
篮球场	65.2%	52.2%	63.0%	63.5%
地面跑道	57.7%	55.0%	57.6%	57.1%
港口	63.1%	61.8%	63.0%	61.6%
桥梁	45.5%	40.2%	42.9%	41.9%
大型车辆	80.7%	77.9%	79.4%	80.7%
小型车辆	60.3%	60.5%	61.8%	62.8%
直升机	25.2%	57.7%	54.9%	53.1%
环形交叉路口	66.0%	60.8%	63.7%	66.8%
足球场	66.6%	63.3%	54.3%	56.2%
游泳池	59.8%	59.8%	62.2%	62.8%
mAP	66.8%	67.3%	68.1%	68.5%

算法	特征提取网络	mAP
无通道注意力机制	Resnet101	62.8%
有通道注意力机制	Resnet101	65.1%

算法	特征提取网络	mAP
+多尺度融合	Resnet50	62.4%
无多尺度融合	Resnet50	62.7%
+多尺度融合	Resnet101	64.0%
无多尺度融合	Resnet101	62.8%

算法	mAP	每秒处理帧数/帧	模型大小/MB
Faster R-CNN^[3]	52.9%	12.5	237
R2CNN^[18]	60.6%	0.05	359
RRPN^[20]	61.0%	0.09	369
R3det^[19]	73.7%	1.5	787
本文提出的算法	68.5%	17.4	218

Light weight rotating object detector based on angle sensitive spatial attention mechanism

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旋转框参数表示方法	水平框	旋转框
方法1	64.9%	63.7%
方法2	64.3%	59.3%
方法3	64.4%	40.5%
方法4	64.7%	42.1%

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