基于关键点检测的红外弱小目标检测

doi:10.7527/S1000-6893.2023.28173

Abstract

Abstract:

Infrared small target detection aims to detect infrared dim small targets from complex background. With wide application of this technology in monitoring and early warning systems， precise guidance， etc.， how to realize fast and accurate detection of infrared small targets is of great importance. Existing traditional algorithms often result in missed detection or false detection， and the detection method based on semantic segmentation in deep learning is easy to be affected by “over segmentation” and “under segmentation”. To address these problems， an infrared small target detection algorithm is proposed based on key point detection （KeypointNet）. The main innovations of this study are as follows. First， The coordinates of the target center point is directly optimized， improving detection efficiency and effectively ensuring the detection rate and false alarm rate of the target. Second， a low-high level feature fusion module is designed to obtain multi-scale information of the target and improve the detection effect. Experimental results on relevant data sets show that in comparison with other algorithms， the algorithm proposed can achieve better effect， with the detection rate reaching 97.58% and the false alarm rate being reduced to less than 2%.

Key words: infrared small target detection, key point detection, deep learning, KeypointNet, image processing

CLC Number:

V243

Qiang WANG, Letian WU, Yong WANG, Huan WANG, Wankou YANG. An infrared small target detection method based on key point[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(10): 328173-328173.

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数据集	算法	P_d/%	F_a/%
NJUST-SIRST	GST	79.00	25.33
	ILCM	38.67	53.69
	LIG	81.17	90.27
	NRAM	78.00	30.80
	PSTNN	79.67	44.07
	TLLCM	81.00	92.01
	DNANet	84.83	6.83
	MDvsFA	89.50	12.51
	KeypointNet	87.42	6.00

NUAA-SIRST	GST	87.02	13.09
	ILCM	43.41	38.85
	LIG	90.12	50.38
	NRAM	86.05	22.62
	PSTNN	86.82	23.81
	TLLCM	95.83	92.73
	DNANet	98.48	5.31
	MDvsFA	89.73	2.91
	KeypointNet	97.58	1.74

算法	100张图片推理时间/s	参数量/M
DNANet	4.58	4.97
MDvsFA	5.01	3.92
KeypointNet	1.20	7.18

算法	NJUST-SIRST		NUAA-SIRST
算法	P_d/%	F_a/%	P_d/%	F_a/%
语义分割	88.17	18.40	98.48	3.49
KeypointNet	87.42	6.00	97.58	1.74

高斯半径	NJUST-SIRST		NUAA-SIRST
高斯半径	P_d/%	F_a/%	P_d/%	F_a/%
r=3	86.00	5.76	92.64	1.12
r=5	87.42	6.00	97.58	1.74
r=10	89.58	8.89	98.06	5.31
r=15	88.33	11.52	97.18	7.69

模块	NJUST-SIRST		NUAA-SIRST
模块	P_d/%	F_a/%	P_d/%	F_a/%
Baseline	83.67	8.18	93.02	1.74
Baseline+ PPM	85.00	6.83	96.03	2.42
Baseline+BST+PPM	87.42	6.00	97.58	1.74

An infrared small target detection method based on key point

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损失函数	NJUST-SIRST		NUAA-SIRST
损失函数	P_d/%	F_a/%	P_d/%	F_a/%
SmoothL1 Loss	87.42	6.00	97.58	1.74
MSE Loss	86.16	6.83	96.03	2.42
Wing Loss 1	1.333	64.62	1.163	50.84
Wing Loss 2	7.917	58.22	61.63	30.41

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