基于空天无人平台信息的伪装目标智能检测

doi:10.7527/S1000-6893.2025.32819

Abstract

Abstract:

Intelligent camouflage target detection aims to identify targets hidden in camouflage environments using limited feature information. Existing camouflage target detection methods have problems such as low saliency features， small inter-class differences， high annotation costs for datasets， limited means of extracting small pixel features， and poor performance in extracting key features of targets in camouflage scenarios. To address these issues， this paper proposes a new intelligent camouflage target detection method based on an auxiliary information of unmanned aerospace platforms. Firstly， an improved deep convolutional generative adversarial network is proposed to expand the dataset， and enhance the image generation quality by designing a new loss function and embedding an attention mechanism module. Then， an improved Yolov11 network-Yolov11-Codattention is designed. The uniqueness of this network lies in its use of auxiliary information from space---unmanned aerospace platforms and the integration of two types of modules： context information modules and receptive field mechanism modules. These modules help solve problems such as low saliency features and small inter-class differences. By introducing these two types of modules， the designed network significantly enhances the target feature extraction capability while reducing the demand for model parameters. Based on the public camouflage dataset， camouflage target detection simulation experiments were conducted. The experimental results show that the proposed Yolov11-Codattention algorithm improves the recall rate and mAP@50 performance indicators by 7.0% and 4.8% respectively compared with the traditional Yolov11 algorithm， and the real-time performance reaches 40FPS. Through comparison with seven commonly used target detection algorithms， it is found that Yolov11-Codattention has higher camouflage target detection accuracy. The results of the field embedded deployment experiments show that the average confidence of Yolov11-Codattention reaches 0.63， and the real-time performance reaches 30FPS， meeting the engineering application requirements. The experimental results fully verify the effectiveness of the designed Yolov11-Codattention algorithm in the camouflage target detection task.

Key words: unmanned aerospace platform, context information, receptive field mechanism, target detection, feature extraction, camouflage environment

CLC Number:

TP274⁺.3

Hang GENG, Yixuan WU, Xuan GOU, Xinjian LI, Kai CHEN. Intelligent camouflaged target detection based on information of aerospace unmanned platforms[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(S1): 732819.

Figures/Tables 19

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Table 1

Time complexity of modules

模块	时间复杂度
SC	$O (W ⋅ H ⋅ K 1 ⋅ K 2 ⋅ C 1 ⋅ C 2)$
DSC	$O (W ⋅ H ⋅ K 1 ⋅ K 2 ⋅ C 1)$
SC-DSCONV	$O ((W ⋅ H ⋅ K 1 ⋅ K 2 ⋅ C 2) / (2 (C 1 + 1)))$

Table 1

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References 21

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算法+数据集	平均精度mAP/%	最高精度/%
CODT	92.80	97.50
DCGAN+CODT	93.45	98.90
改进DCGAN+CODT	96.35	99.32

算法	准确率 P/%	召回率 R/%	平均精度mAP50/%	检测速度/FPS
Yolov11s	94.10	90.28	96.21	30
Yolov11m	95.52	93.79	97.38	24
Yolov8s	93.58	88.53	95.45	28
Yolov11-Codattention	96.35	97.58	99.12	40
SINET	95.32	95.23	97.78	15
PFA	95.54	95.78	98.34	12
FASTER R-CNN	92.23	86.34	94.78	5
DETR	95.98	97.23	98.89	20

算法	准确率P/%	召回率R/%	平均精度mAP50/%	检测速度/FPS
Yolov11s	94.10	90.28	96.21	30
SC-DSCONV	95.23	92.28	97.72	45
RFV2	94.84	94.59	97.53	25
本文算法	96.35	97.58	99.12	40

Intelligent camouflaged target detection based on information of aerospace unmanned platforms

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