利用非局部上下文信息的遥感图像小目标检测

doi:10.19665/j.issn1001-2400.2022.05.014

Abstract

Abstract:

In recent years,the object detection method based on deep learning has achieved remarkable results and has been successfully applied to remote sensing.However,due to the wide coverage of remote sensing images,and the small object with less effective information and which is difficult to locate,it is challenging to accurately detect a small object from remote sensing images.To solve this problem,non-local information and context are utilized to improve the quality of small object detection in this paper.First,the detector uses a combination of Refine Feature Pyramid Networks(Refine FPN)and Cross-layer Attention Network(CA-Net)as the backbone,where the Refine FPN obtains richer feature information of a small object,and the CA-Net extracts non-local information and distributes it to each layer evenly.Second,the context transfer module is proposed to transfer the non-local context information to the corresponding region of interest.Finally,the cascade network is used as the detection network to improve the quality of the bounding box of the small object.Experiments are carried out on three remote sensing image datasets,Small-DOTA,DIOR,and OHD-SJTU-S.Experimental results show that the mean average precision(mAP)of the detector proposed in this paper reaches the highest in the three datasets.Among the three categories of ships,vehicles,and windmills that contain more small targets in the DIOR,the average precision(AP)of the detector in this paper is also the highest.This shows that compared with the existing methods,the method in this paper can further improve the detection performance of the small object in remote sensing images.

Key words: remote sensing, object detection, image processing, non-local information, context

CLC Number:

TP75

LI Yangyang, MAO Heting, ZHANG Xiaolong, CHEN Yanqiao, CHAI Xinghua. Small object detection in remote sensing images using non-local context information[J].Journal of Xidian University, 2022, 49(5): 117-124.

Figures/Tables 7

References 19

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方法	小型车辆	船	储油罐	大型车辆	mAP
Faster R-CNN	81.22	80.38	87.87	73.57	80.76
FPN	83.15	80.74	88.03	74.75	81.67
Refine FPN	85.17	89.20	88.11	75.07	84.39
Cascade R-CNN	84.46	89.24	88.18	74.66	84.14
CA-Net^*	84.85	89.10	88.41	78.68	85.26
文中算法	85.71	89.53	88.95	79.81	86.00

对比方法	飞机	船	mAP
Faster R-CNN	86.09	74.58	80.34
FPN	85.47	78.76	82.12
Refine FPN	86.62	79.90	83.26
Cascade R-CNN	89.93	82.31	86.12
CA-Net^*	88.52	80.72	84.62
文中算法	90.17	83.83	87.00

类别	Faster R-CNN	FPN	Refine FPN	Cascade R-CNN	CA-Net^*	文中算法
飞机	52.3	62.2	62.8	62.8	69.8	63.0
机场	66.4	73.4	73.7	81.3	78.9	83.7
棒球场	67.2	71.7	71.6	71.9	77.5	72.0
篮球场	80.5	81.0	87.5	88.2	87.2	88.3
桥梁	34.3	44.9	50.7	52.2	52.2	52.9
烟囱	71.8	72.5	79.2	79.5	79.6	79.7
水坝	54.2	58.8	64.8	64.8	65.2	67.9
高速公路服务区	62.5	76.8	78.4	79.0	84.0	83.9
高尔夫球场	69.6	72.8	75.0	77.7	78.3	82.1
田径场	69.1	76.7	82.4	83.3	82.6	82.2
港口	48.6	43.5	55.3	59.5	55.7	59.7
立交桥	52.4	57.4	62.1	63.2	61.7	64.5
船	69.3	71.9	80.9	81.0	80.9	81.1
体育馆	62.3	68.6	73.4	74.2	75.3	72.6
储油罐	57.8	62.3	62.7	62.9	62.5	70.7
网球场	79.8	81.3	81.4	81.5	88.2	81.5
火车站	51.9	56.9	62.3	62.7	64.4	67.0
车辆	38.6	49.7	50.8	50.8	51.1	51.4
风车	78.0	80.6	88.6	89.1	89.1	89.2
mAP	60.8	66.7	71.0	71.8	73.0	73.5

Small object detection in remote sensing images using non-local context information

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