Camouflaged object detection based on progressive feature enhancement aggregation

doi:10.11772/j.issn.1001-9081.2021060900

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2192-2200.DOI: 10.11772/j.issn.1001-9081.2021060900

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles Next Articles

Camouflaged object detection based on progressive feature enhancement aggregation

Xiangyue TAN¹, Xiao HU²(), Jiaxin YANG¹, Junjiang XIANG¹

^1.School of Electronics and Communication Engineering，Guangzhou University，Guangzhou Guangdong 510006，China
^2.School of Mechanical and Electrical Engineering，Guangzhou University，Guangzhou Guangdong 510006，China

Received:2021-06-03 Revised:2021-08-01 Accepted:2021-08-06 Online:2021-08-01 Published:2022-07-10
Contact: Xiao HU
About author:TAN Xiangyue， born in 1998， M. S. candidate. Her research interests include deep learning， camouflaged object detection.
YANG Jiaxin， born in 1996， M. S. candidate. His research interests include deep learning， salient object detection.
XIANG Junjiang， born in 1996， M. S. candidate. His research interests include video analysis， object detection.
Supported by:
National Natural Science Foundation of China(62076075)

基于递进式特征增强聚合的伪装目标检测

谭湘粤¹, 胡晓²(), 杨佳信¹, 向俊将¹

^1.广州大学电子与通信工程学院，广州 510006
^2.广州大学机械与电气工程学院，广州 510006

通讯作者: 胡晓
作者简介:谭湘粤（1998—），女，湖南衡阳人，硕士研究生，主要研究方向：深度学习、伪装目标检测
杨佳信（1996—），男，广东揭阳人，硕士研究生，主要研究方向：深度学习、显著性目标检测
向俊将（1996—），男，湖南益阳人，硕士研究生，主要研究方向：视频分析、目标检测。
基金资助:
国家自然科学基金资助项目(62076075)

Abstract

Abstract:

Camouflaged Object Detection （COD） aims to detect objects hidden in complex environments. The existing COD algorithms ignore the influence of feature expression and fusion methods on detection performance when combining multi-level features. Therefore， a COD algorithm based on progressive feature enhancement aggregation was proposed. Firstly， multi-level features were extracted through the backbone network. Then， in order to improve the expression ability of features， an enhancement network composed of Feature Enhancement Module （FEM） was used to enhance the multi-level features. Finally， Adjacency Aggregation Module （AAM） was designed in the aggregation network to achieve information fusion between adjacent features to highlight the features of the camouflaged object area， and a new Progressive Aggregation Strategy （PAS） was proposed to aggregate adjacent features in a progressive way to achieve effective multi-level feature fusion while suppressing noise. Experimental results on 3 public datasets show that the proposed algorithm achieves the best performance on 4 objective evaluation indexes compared with 12 state-of-the-art algorithms， especially on COD10K dataset， the weighted F-measure and the Mean Absolute Error （MAE） of the proposed algorithm reach 0.809 and 0.037 respectively. It can be seen that the proposed algorithm achieves better performance on COD tasks.

Key words: Convolutional Neural Network （CNN）, Camouflaged Object Detection （COD）, feature enhancement, Adjacency Aggregation Module （AAM）, Progressive Aggregation Strategy （PAS）

摘要：

伪装目标检测（COD）旨在检测隐藏在复杂环境中的目标。现有COD算法在结合多层次特征时，忽略了特征的表达和融合方式对检测性能的影响。为此，提出一种基于递进式特征增强聚合的COD算法。首先，通过主干网络提取多级特征；然后，为了提高特征的表达能力，使用由特征增强模块（FEM）构成的增强网络对多层次特征进行增强；最后，在聚合网络中设计邻近聚合模块（AAM）实现相邻特征之间的信息融合，以突显伪装目标区域的特征，并提出新的递进式聚合策略（PAS）通过渐进的方式聚合邻近特征，从而在实现多层特征有效融合的同时抑制噪声。在3个公开数据集上的实验表明，所提算法相较于12种最先进的算法在4个客观评价指标上均取得最优表现，尤其是在COD10K数据集上所提算法的加权的F测评法和平均绝对误差（MAE）分别达到了0.809和0.037。由此可见，所提算法在COD任务上拥有较优的性能。

关键词: 卷积神经网络, 伪装目标检测, 特征增强, 邻近聚合模块, 递进式聚合策略

CLC Number:

TP391.4

Xiangyue TAN, Xiao HU, Jiaxin YANG, Junjiang XIANG. Camouflaged object detection based on progressive feature enhancement aggregation[J]. Journal of Computer Applications, 2022, 42(7): 2192-2200.

谭湘粤, 胡晓, 杨佳信, 向俊将. 基于递进式特征增强聚合的伪装目标检测[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2192-2200.

Figures/Tables 12

Fig.1 Overall framework of the proposed algorithm

Fig.2 Structure of feature enhancement module

Tab.1 Ablation experimental results of FEM on CHAMELEON and CAMO datasets

方法	CHAMELEON				CAMO-Test
方法	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$
SA+CA+RF	0.889	0.938	0.819	0.032	0.799	0.861	0.712	0.079
SA+RF+CA	0.890	0.945	0.816	0.030	0.798	0.859	0.715	0.078
RF+SA+CA	0.891	0.947	0.822	0.031	0.805	0.862	0.720	0.075
RF+CA+SA	0.886	0.940	0.811	0.031	0.800	0.858	0.714	0.077
CA+RF+SA	0.894	0.944	0.825	0.029	0.803	0.863	0.717	0.075
CA+SA+RF	0.895	0.948	0.826	0.028	0.808	0.867	0.726	0.075

Tab.1 Ablation experimental results of FEM on CHAMELEON and CAMO datasets

方法	CHAMELEON				CAMO-Test
方法	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$
SA+CA+RF	0.889	0.938	0.819	0.032	0.799	0.861	0.712	0.079
SA+RF+CA	0.890	0.945	0.816	0.030	0.798	0.859	0.715	0.078
RF+SA+CA	0.891	0.947	0.822	0.031	0.805	0.862	0.720	0.075
RF+CA+SA	0.886	0.940	0.811	0.031	0.800	0.858	0.714	0.077
CA+RF+SA	0.894	0.944	0.825	0.029	0.803	0.863	0.717	0.075
CA+SA+RF	0.895	0.948	0.826	0.028	0.808	0.867	0.726	0.075

Fig.3 Structure of adjacency aggregation module

Fig.4 Visualization results of intermediate features of PAS

Tab.2 Ablation experimental results of AAM on CHAMELEON and CAMO datasets

方法	CHAMELEON				CAMO-Test
方法	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$
三邻近-二邻近	0.885	0.939	0.813	0.033	0.801	0.858	0.715	0.077
二邻近-三邻近	0.884	0.936	0.813	0.033	0.805	0.862	0.717	0.076
二邻近	0.895	0.948	0.826	0.028	0.808	0.867	0.726	0.075

Tab.2 Ablation experimental results of AAM on CHAMELEON and CAMO datasets

方法	CHAMELEON				CAMO-Test
方法	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$
三邻近-二邻近	0.885	0.939	0.813	0.033	0.801	0.858	0.715	0.077
二邻近-三邻近	0.884	0.936	0.813	0.033	0.805	0.862	0.717	0.076
二邻近	0.895	0.948	0.826	0.028	0.808	0.867	0.726	0.075

Fig.5 Three AAM composite structures with different adjacent modes

Tab. 3 Ablation experimental results between networks on COD10K dataset

方法	COD10K-Test
方法	$S α$	$E φ$	$F β ω$	$M$
No.1	0.794	0.869	0.637	0.041
No.2	0.797	0.874	0.644	0.040
No.3	0.805	0.881	0.667	0.038
No.4	0.809	0.886	0.673	0.037

Tab. 3 Ablation experimental results between networks on COD10K dataset

方法	COD10K-Test
方法	$S α$	$E φ$	$F β ω$	$M$
No.1	0.794	0.869	0.637	0.041
No.2	0.797	0.874	0.644	0.040
No.3	0.805	0.881	0.667	0.038
No.4	0.809	0.886	0.673	0.037

Fig.6 Feature visualization results of skipping aggregation and progressive aggregation

Tab.4 Objective evaluation of different algorithms on three COD datasets

算法	CHAMELEON				CAMO-Test				COD10K-Test
算法	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$
FPN	0.794	0.783	0.590	0.075	0.684	0.677	0.483	0.131	0.697	0.691	0.411	0.075
UNet++	0.695	0.762	0.501	0.094	0.599	0.653	0.392	0.149	0.623	0.672	0.350	0.086
ANet-SRM	—	—	—	—	0.682	0.685	0.484	0.126	—	—	—	—
PFANet	0.679	0.648	0.378	0.144	0.659	0.622	0.391	0.172	0.636	0.618	0.286	0.128
PoolNet	0.776	0.779	0.555	0.081	0.702	0.698	0.494	0.129	0.705	0.713	0.416	0.074
CPD	0.853	0.866	0.706	0.052	0.726	0.729	0.550	0.115	0.747	0.770	0.508	0.059
EGNet	0.848	0.870	0.702	0.050	0.732	0.768	0.583	0.104	0.737	0.779	0.509	0.056
SINet	0.869	0.891	0.740	0.044	0.751	0.771	0.606	0.100	0.771	0.806	0.551	0.051
PraNet	0.860	0.907	0.763	0.044	0.769	0.824	0.663	0.094	0.789	0.861	0.629	0.045
MCIF-Net	—	—	—	—	0.784	0.845	0.677	0.084	0.787	0.872	0.636	0.042
Rank-Net	0.893	0.938	—	0.033	0.793	0.826	—	0.085	0.793	0.868	—	0.041
TANet	0.888	0.911	0.786	0.036	0.793	0.834	0.690	0.083	0.803	0.848	0.629	0.041
本文算法	0.895	0.948	0.826	0.028	0.808	0.867	0.726	0.075	0.809	0.886	0.673	0.037

Tab.4 Objective evaluation of different algorithms on three COD datasets

算法	CHAMELEON				CAMO-Test				COD10K-Test
算法	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$	$S α$	$E φ$	$F β ω$	$M$
FPN	0.794	0.783	0.590	0.075	0.684	0.677	0.483	0.131	0.697	0.691	0.411	0.075
UNet++	0.695	0.762	0.501	0.094	0.599	0.653	0.392	0.149	0.623	0.672	0.350	0.086
ANet-SRM	—	—	—	—	0.682	0.685	0.484	0.126	—	—	—	—
PFANet	0.679	0.648	0.378	0.144	0.659	0.622	0.391	0.172	0.636	0.618	0.286	0.128
PoolNet	0.776	0.779	0.555	0.081	0.702	0.698	0.494	0.129	0.705	0.713	0.416	0.074
CPD	0.853	0.866	0.706	0.052	0.726	0.729	0.550	0.115	0.747	0.770	0.508	0.059
EGNet	0.848	0.870	0.702	0.050	0.732	0.768	0.583	0.104	0.737	0.779	0.509	0.056
SINet	0.869	0.891	0.740	0.044	0.751	0.771	0.606	0.100	0.771	0.806	0.551	0.051
PraNet	0.860	0.907	0.763	0.044	0.769	0.824	0.663	0.094	0.789	0.861	0.629	0.045
MCIF-Net	—	—	—	—	0.784	0.845	0.677	0.084	0.787	0.872	0.636	0.042
Rank-Net	0.893	0.938	—	0.033	0.793	0.826	—	0.085	0.793	0.868	—	0.041
TANet	0.888	0.911	0.786	0.036	0.793	0.834	0.690	0.083	0.803	0.848	0.629	0.041
本文算法	0.895	0.948	0.826	0.028	0.808	0.867	0.726	0.075	0.809	0.886	0.673	0.037

Fig.7 Precision-recall curves comparison of nine algorithms on three COD datasets

Fig.8 Visualization comparison of nine algorithms

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Camouflaged object detection based on progressive feature enhancement aggregation

基于递进式特征增强聚合的伪装目标检测

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