基于混合尺度感知与边缘特征交互的伪装目标检测

doi:10.13229/j.cnki.jdxbgxb.20241319

Abstract

Abstract:

In view of the problems of lack of edge feature information and insufficient multi-level feature fusion in the existing amouflage object detection（COD） methods， a novel camouflaged object detection network MSPEFI-Net based on mixed-scale perception and edge feature interaction was proposed. MSPEFI-Net consists of a mixed-scale perception module（MSP），an edge feature interaction module（EFI）and a global feature aggregation module（GFA）.MSP extracts different scale features with different convolutional kernels，and fuses cross-scale features to achieve mixed-scale perception，so that MSPEFI-Net can effectively expand the receptive field and fully mine the local detail features of the object to ensure the integrity of feature information.EFI can interact and integrate high-level semantic information with some low-level detailed information to mine and supplement edge semantic information.GFA aggregates cross-level features from context local information and edge information to effectively integrate camouflage object features and its edge features，and generates a full prediction plot. Experimental results prove that the $S α$ 、 $F β w$ 、 $F β$ 、 $E φ$ and $M A E$ of MSPEFI-Net on the public datasets CAMO，CHAMELEON，COD10K and NC4K are superior to 12 comparison methods and show better detection performance of MSPEFI-Net.Ablation experiments also verify the effectiveness of each module to improve the performance of MSPEFI-Net.

Key words: camouflage object detection, mixed-scale perception, edge feature interaction, feature enhancements

CLC Number:

TP391.4

Dong-bing PU,Jin-feng ZHANG,Mei-hua ZHANG,Yi-ke WANG,Ying-juan SUN. Camouflaged object detection based on mixed scale perception and edge feature interaction[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(7): 1996-2005.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Performance comparison on CAMO， COD10K， CHAMELEON， and NC4K datasets"

方法	CAMO					CHAMELEON
方法	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑
SINet^［1］	0.745	0.644	0.092	0.702	0.829	0.872	0.806	0.034	0.827	0.946
MGL-R^［3］	0.775	0.673	0.088	0.726	0.842	0.893	0.812	0.031	0.833	0.941
C²FNet^［2］	0.796	0.719	0.080	0.762	0.864	0.888	0.828	0.032	0.844	0.946
SINetv2^［11］	0.820	0.743	0.070	0.782	0.895	0.888	0.816	0.030	0.835	0.961
BGNet^［9］	0.812	0.749	0.073	0.789	0.882	0.901	0.850	0.027	0.860	0.954
ZoomNet^［4］	0.820	0.752	0.066	0.794	0.892	0.902	0.845	0.023	0.864	0.958
DGNet^［6］	0.839	0.769	0.057	0.806	0.915	0.890	0.816	0.029	0.834	0.956
FEDER^［5］	0.802	0.738	0.071	0.781	0.873	0.887	0.834	0.030	0.851	0.954
FDNet^［7］	0.841	0.775	0.063	0.807	0.908	0.897	0.835	0.027	0.854	0.964
FSPNet^［8］	0.856	0.799	0.050	0.830	0.928	0.908	0.851	0.023	0.867	0.965
FINet^［16］	0.828	0.752	0.065	0.791	0.900	0.883	0.808	0.031	0.825	0.957
UEDGNet^［15］	0.867	0.816	0.049	0.841	0.931	0.909	0.862	0.023	0.874	0.969
MSPEFI-Net	0.867	0.825	0.046	0.854	0.929	0.910	0.862	0.020	0.874	0.964
方法	COD10K					NC4K
方法	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑
SINet^［1］	0.776	0.631	0.043	0.679	0.874	0.808	0.723	0.058	0.769	0.883
MGL-R^［3］	0.814	0.666	0.035	0.710	0.890	0.833	0.739	0.053	0.782	0.893
C²FNet^［2］	0.813	0.686	0.036	0.723	0.900	0.838	0.762	0.049	0.795	0.904
SINetv2^［11］	0.815	0.680	0.037	0.718	0.906	0.847	0.770	0.048	0.805	0.914
BGNet^［9］	0.831	0.722	0.033	0.753	0.911	0.851	0.788	0.044	0.820	0.916
ZoomNet^［4］	0.838	0.729	0.029	0.766	0.911	0.853	0.784	0.043	0.818	0.912
DGNet^［6］	0.822	0.693	0.033	0.728	0.911	0.857	0.784	0.042	0.814	0.922
FEDER^［5］	0.822	0.716	0.032	0.751	0.905	0.847	0.789	0.044	0.824	0.915
FDNet^［7］	0.840	0.729	0.030	0.757	0.935	0.834	0.750	0.052	0.784	0.905
FSPNet^［8］	0.851	0.735	0.026	0.769	0.930	0.879	0.816	0.035	0.843	0.937
FINet^［16］	0.817	0.686	0.034	0.723	0.908	0.847	0.771	0.047	0.805	0.911
UEDGNet^［15］	0.856	0.760	0.026	0.785	0.932	0.882	0.828	0.035	0.852	0.938
MSPEFI-Net	0.867	0.784	0.022	0.813	0.935	0.879	0.830	0.034	0.857	0.936

Table 1

Fig.5

Fig.6

Fig.7

Table 2

Ablation study results on CAMO and COD10K"

模型	方法				CAMO					COD10K
模型	Basic	MSP	EFI	GFA	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑
a	√				0.855	0.813	0.049	0.847	0.917	0.858	0.774	0.024	0.809	0.933
b	√	√			0.857	0.813	0.051	0.846	0.916	0.866	0.780	0.023	0.810	0.932
c	√	√	√		0.865	0.822	0.049	0.852	0.927	0.866	0.784	0.023	0.814	0.937
d	√	√	√	√	0.867	0.825	0.046	0.854	0.929	0.867	0.784	0.022	0.813	0.935

Table 2

Table 3

Ablation study results on NC4K"

模型	方法				NC4K
模型	Basic	MSP	EFI	GFA	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑
a	√				0.869	0.820	0.037	0.853	0.931
b	√	√			0.875	0.824	0.035	0.853	0.932
c	√	√	√		0.878	0.827	0.035	0.855	0.934
d	√	√	√	√	0.879	0.830	0.034	0.857	0.936

Table 3

Table 4

Ablation study results of GFA on CAOMO， COD10K and NC4K"

模型	方法	CAMO					COD10K					NC4K
模型	方法	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑	$S α$ ↑	$F β w$ ↑	MAE↓	$F β$	$E φ$ ↑
a	W/CA	0.865	0.823	0.047	0.853	0.927	0.867	0.785	0.022	0.811	0.933	0.879	0.829	0.034	0.855	0.932
b	W/SA	0.862	0.819	0.050	0.850	0.920	0.865	0.777	0.024	0.808	0.932	0.876	0.825	0.035	0.853	0.933
c	W/MSCA	0.867	0.825	0.046	0.854	0.929	0.867	0.784	0.022	0.813	0.935	0.879	0.830	0.034	0.857	0.936

Table 4

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