Infrared Tiny Target Detection Method Based on a Multi-Hop Deep Network

Fig. 1. Imaging characteristics of infrared targets in complex background. (a) Target imaging performance; (b) grayscale distribution of infrared images; (c) top view of grayscale distribution

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Fig. 2. Overall framework of proposed method

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Fig. 3. Structure of MMFB and class activation mapping diagram of corresponding branches

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Fig. 4. ROC curves for different compared methods

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Fig. 5. Visualization of detection results

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Fig. 6. Visualization of infrared targets on IRDST dataset

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Table 1. Comparisons of different methods on infrared target detection

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Table 1. Comparisons of different methods on infrared target detection

Method	Target	Recall /%	Precision /%	F1-score /%	$F_{a}$ /%	FNR /%	Time /ms
MDFA-CGAN	Tiny	56.8	52.4	54.5	41.6	43.2	27.1
MDFA-CGAN	Small	71.1	45.4	55.4	16.5	28.9	27.1
ACM	Tiny	19.9	26.7	22.8	44.1	80.1	30.3
ACM	Small	54.9	57.4	56.1	7.8	45.1	30.3
DNANet	Tiny	69.9	73.6	71.7	20.2	30.1	31.3
DNANet	Small	86.8	86.7	86.8	2.6	13.2	31.3
YOLO-FR	Tiny	92.1	60.6	73.1	48.3	7.9	6.4
YOLO-FR	Small	90.6	56.3	69.5	13.5	9.4	6.4
Proposed method	Tiny	97.0	94.5	95.7	4.5	3.0	11.3
Proposed method	Small	91.1	96.4	93.7	0.7	8.9	11.3

Table 2. Ablation experiments

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Table 2. Ablation experiments

Baseline	MMFB	NWD	Target	Recall	Precision	F1-score	$F_{a}$	FNR
√			Tiny	95.1	79.0	86.3	20.4	4.9
√			Small	46.5	99.6	63.4	19.5	53.5
√	√		Tiny	95.9	84.7	90.0	4.2	4.1
√	√		Small	89.4	89.7	89.6	2.0	10.6
√		√	Tiny	92.0	90.4	91.2	5.3	8.0
√		√	Small	93.5	82.6	87.7	3.8	6.5
√	√	√	Tiny	97.0	94.5	95.7	4.5	3.0
√	√	√	Small	91.1	96.4	93.7	0.7	8.9

Table 3. Comparison on IRDST dataset
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Table 3. Comparison on IRDST dataset
Method Recall Precision F1-score $F_{a}$ FNR
MDFA-CGAN 41.6 44.8 43.1 55.2 58.4
ACM 35.5 35.9 35.7 64.1 64.5
DNANet 89.2 90.5 89.9 9.5 10.8
YOLO-FR 83.2 58.5 68.7 41.5 16.8
Proposed method 93.6 95.7 94.6 4.3 6.4

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Shule Yan, Runyu Chen, Nian Cai, Shaoqiu Xu, Jian Chen. Infrared Tiny Target Detection Method Based on a Multi-Hop Deep Network[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2237008

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