Target Detection of SSD Aircraft Remote Sensing Images Based on Anchor Frame Strategy Matching

doi:10.3778/j.issn.1673-9418.2105108

Abstract

Abstract:

Aiming at the problem that the accuracy and real-time performance of current aircraft remote sensing image target detection algorithms cannot be balanced, a target detection algorithm based on single shot MultiBox detector (SSD) is proposed for anchor frame scale densification and anchor frame strategy matching. The algorithm uses an improved deep residual network to replace the original feature extraction network of the SSD algorithm. Combined with the small-scale and dense features of aircraft remote sensing images, this paper redesigns the size and proportion of anchor frame and adds a feature layer containing two scales. Then, the anchor frame densification operation is performed on each feature layer to make the anchor frame laying density of the feature layer basically equal, and to improve the probability of matching the anchor frames of different scales to the real target. On the issue of the large gap in the number of positive sample anchor frames of different scales, an anchor frame strategy matching method that makes the number of positive sample anchor frames of different scales tend to the overall positive sample average is proposed, which improves the effectiveness of training and robustness of target detection to a certain extent. Related experiments are conducted on the aircraft remote sensing dataset, the average precision reaches 91.15%, and the frame per second is 33.4. The results show that the improved algorithm can not only increase the detection accuracy on the basis of adding fewer training parameters, but also retain the real-time detec-tability of the SSD algorithm.

Key words: target detection, remote sensing image, real-time detection, anchor box matching

摘要：

针对当前飞机遥感图像目标检测算法的精度和实时性不能兼顾的问题，提出了基于SSD的锚框尺度密集化和锚框策略匹配目标检测算法。该算法选用经过改进后的深度残差网络替代SSD算法原有的特征提取网络。结合飞机遥感图像存在小尺度且密集的特点，重新设计了锚框尺度大小、比例和额外增加了一个包含两种尺度的特征层。而后对各个特征层进行锚框密集化操作使得特征层的锚框铺设密度基本相等，提高不同尺度的锚框匹配到真实目标的概率。在不同尺度的正样本锚框数量差距较大的问题上，提出了一种使得不同尺度的正样本锚框数量趋向于总体正样本平均值的锚框策略匹配方法，一定程度上提高训练的有效性和目标检测的鲁棒性。在飞机遥感数据集上进行相关实验，精度均值达到91.15%，每秒帧率为33.4。结果表明，改进后的算法不仅可以在增加较少训练参数的基础上提升检测精度，还能保留SSD算法的实时检测性。

关键词: 目标检测, 遥感图像, 实时检测, 锚框匹配

CLC Number:

TP181

WANG Haotong, GUO Zhonghua. Target Detection of SSD Aircraft Remote Sensing Images Based on Anchor Frame Strategy Matching[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(11): 2596-2608.

王浩桐, 郭中华. 锚框策略匹配的SSD飞机遥感图像目标检测[J]. 计算机科学与探索, 2022, 16(11): 2596-2608.

Figures/Tables 18

Fig.1 Framework diagram of SSD algorithm

Fig.2 Residual structure

Table 1 Test results of different feature extraction networks on PASCAL VOC 2007/2012

Training data	Test data	mAP/%
Training data	Test data	VGG16	ResNet101
VOC 2007+VOC 2012	VOC 2007	73.2	76.4
VOC 2007+VOC 2012	VOC 2012	70.4	73.8

Fig.3 Network structure diagram after ResNet50 replaces VGG16

Fig.4 Schematic diagram of effective receptive field

Table 2 Information about anchor frame

特征层	特征层尺寸	理论感受野	锚框铺设步长	锚框尺度	锚框宽高比	锚框铺设密度
Conv1	75×75	35×35	4	8,12	1	2,3
Conv2	38×38	187×187	8	24	1, 2/3, 3/5	3
Conv3	19×19	203×203	16	48	1, 2/3, 3/5	3
Conv4	10×10	235×235	30	90	1, 2/3, 3/5	3
Conv5	5×5	299×299	60	180	1, 2/3, 3/5	3
Conv6	3×3	427×427	100	240	1, 2/3, 3/5	3
Conv7	1×1	555×555	300	300	1, 2/3, 3/5	1

Fig.5 Schematic diagram of anchor frame densification

Fig.6 Results before anchor box strategy matching

Table 3 Statistical information

图6	$L$	$N$	$N$ （不同尺度）	$M$	$D$
（a）	2	12	3,9	6.0	-3,3
（b）	2	33	3,30	16.5	-13.5,13.5

Table 3 Statistical information

图6	$L$	$N$	$N$ （不同尺度）	$M$	$D$
（a）	2	12	3,9	6.0	-3,3
（b）	2	33	3,30	16.5	-13.5,13.5

Fig.7 Results after anchor box strategy matching

Fig.8 Average value before and after matching

Fig.9 Sample examples of dataset in this paper

Fig.10 Target detection results of different algorithms

Fig.11 PR curves of several classic target detection algorithms

Table 4 AP and FPS of different algorithms on aircraft remote sensing images

Method	Backbone	AP/%		FPS
Method	Backbone	IOU=0.50,area=all	IOU=0.50:0.95,area=small	FPS
Faster-RCNN+FPN	VGG16	91.35	40.56	3.4
YOLO	GoogleNet	78.25	28.64	49.6
YOLO v2^[25]	DarkNet19	84.36	33.46	42.8
YOLO v3	DarkNet53	88.14	38.22	38.6
SSD	VGG16	88.72	39.65	41.3
DSSD^[26]	ResNet101	89.55	41.25	26.5
AMDSSD	ResNet50	91.15	41.36	33.4

Table 5 Ablation experiment results of skeleton network replacement

Method	AP/%(IOU=0.50, area=all)
SSD	88.74
SSD+ResNet50(改)	89.36

Table 6 Results of ablation experiments matching anchor frame densification and anchor frame strategy

Method	AP/%（IOU=0.50,area=all）
SSD+ResNet50（改）	89.36
SSD+ResNet50（改）+锚框密集	90.58
SSD+ResNet50（改）+锚框密集+锚框策略匹配	91.15

Fig.12 Some test results of AMDSSD in different scenarios

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