基于解耦的小样本目标检测方法研究

doi:10.12305/j.issn.1001-506X.2024.09.06

Abstract

Abstract:

Aiming at the coupling problem of target detection with few shot intensification, a few shot target detection algorithm based on decoupling is proposed focusing on high-value air targets as the research object. Firstly, a gradient adjustment layer is introduced into the regional candidate network to strengthen the regional candidate network and alleviate the task coupling problem. Secondly, the target detection head is disassembled into two branches, classification and regression, and a parameter-free average attention module is added at the front end to alleviate the feature coupling problem. The proposed algorithm improves the detection performance of few shot target detection and enhances the detection ability of new classes. The experimental results show that the proposed algorithm performs best in the 1, 2, 3, 5 and 10 shot experiments, with average accuracy of 32.5%, 35.6%, 39.6%, 41.2% and 57.4%, respectively. Compared with the two-stage fine-tuning method, the detection performance of the proposed algorithm is greatly improved, which solves the problem of the decline of network detection ability under the coupling contradiction of few shot intensification, and improves the detection accuracy of few shot high-value air targets.

Key words: few shot target detection, air target, coupling issue, deep learning

CLC Number:

TP391.4

Wei CAI, Xin WANG, Xinhao JIANG, Zhiyong YANG, Dong CHEN. Research on few shot target detection method based on decoupling[J]. Systems Engineering and Electronics, 2024, 46(9): 2941-2950.

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方法	样本量
方法	1	2	3	5	10
TFA/FCL	25.6	29.4	27.4	37.1	44.2
TFA/COS	17.2	23.5	23.1	23.4	29.2
Attention RPN	23.8	29.2	29.1	39.4	54.3
本文	32.5	35.6	39.6	41.2	57.4

方法	目标种类						mAP
方法	F35	J20	Su57	MQ9	RQ4	B2	mAP
TFA/FCL	12.3	55.5	35.6	60.9	43	55.8	44.2
TFA/COS	35.9	45.6	24.3	37.6	16	7.8	29.2
Attention RPN	9.8	61.5	54.2	62.5	65.5	72.5	54.3
本文	19.8	63.3	74.4	61	68.4	62.7	57.4

基线模型	筛选原则	解冻	PFAAM	DBN	样本量
基线模型	筛选原则	解冻	PFAAM	DBN	1	2	3	5	10
√	-	-	-	-	25.6	29.4	27.4	37.1	44.2
√	√	-	-	-	27.5	32.6	35.3	39.4	44.6
√	√	√	-	-	28.1	33.6	36.4	39.6	50.9
√	√	√	√	-	28.5	33.7	36.6	40.2	52.7
√	√	√	√	√	30.5	34.2	38.4	40.7	54.2

调整因子	样本量
调整因子	1	2	3	5	10
0.25	31.8	34.7	39.2	41	55.7
0.5	32.5	35.6	39.6	41.2	57.4
0.75	31.5	34.8	38.8	40.8	56.6
1	30.5	34.2	38.4	40.7	54.2

方法	样本量
方法	1	2	3	5	10
TFA/FCL	36.8	39.1	43.6	55.7	57
TFA/COS	39.8	36.1	44.7	55.7	56
Attention RPN	35	36	39.1	51.7	55.7
本文	40.1	45.3	51.1	58.1	60

Research on few shot target detection method based on decoupling

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