一种基于定位和非对称补偿的伪装目标分割方法

doi:10.12305/j.issn.1001-506X.2022.09.02

Abstract

Abstract:

Camouflage is the instinct to deceive the perception of the observer, which presents the high similarity of the textural characteristics with the surroundings. In order to address the ambiguous regions generated by the similarity between background and foreground, this paper proposes a camouflaged object segmentation network based on locating and compensation network (LCNet). Inspired by predator process: Search→Establish→Focusing, the paradigm of the proposed method is achieved via dual-backbone with the strong sense of knowledge extraction, locating module with double attention, and cascading asymmetric compensation module with pixel refining. Experimental results have shown that the performances of LCNet are superior than six state-of-the-art models at the three major challenging camouflaged datasets in terms of four metrics, and the effectiveness of LCNet is demonstrated.

Key words: textural camouflaged object, asymmetric attention compensation, double attention locating, dual-backbone

CLC Number:

TP391.41

Yifei XU, Xiaodong LI, Xinde LI. A method of camouflaged object segmentation with locating and asymmetric compensation[J]. Systems Engineering and Electronics, 2022, 44(9): 2707-2715.

Figures/Tables 9

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Table 1

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Table 2

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References 38

1	STEVENS M , MERILAITA S . Animal camouflage: current issues and new perspectives[J]. Philosophical Transaction of the Royal Society B: Biological Sciences, 2009, 364 (1516): 423- 427. doi: 10.1098/rstb.2008.0217
2	ZHENG Y W , ZHANG X W , WANG F , et al. Detection of people with camouflage pattern via dense deconvolution network[J]. IEEE Signal Processing Letters, 2018, 26 (1): 29- 33.
3	LE T N , NGUYEN T V , NIE Z , et al. Anabranch network for camouflaged object segmentation[J]. Computer Vision and Image Understanding, 2019, 184, 45- 56. doi: 10.1016/j.cviu.2019.04.006
4	FAN D P, JI G P, SUN G, et al. Camouflaged object detection[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 2777-2787.
5	LIU T , YUAN Z J , SUN J , et al. Learning to detect a salient object[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2010, 33 (2): 353- 367.
6	WANG L J, LU H C, WANG Y F, et al. Learning to detect salient objects with image-level supervision[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 136-145.
7	SHI J P , YAN Q , XU L , et al. Hierarchical image saliency detection on extended CSSD[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2015, 38 (4): 717- 729.
8	LI G B, YU Y Z. Visual saliency based on multiscale deep features[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5455-5463.
9	MOVAHEDI V, ELDER J H. Design and perceptual validation of performance measures for salient object segmentation[C]//Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010: 49-56.
10	TROSCIANKO T , BENTON C P , LOVELL P G , et al. Camouflage and visual perception[J]. Philosophical Transaction of the Royal Society B: Biological Sciences, 2009, 364 (1516): 449- 461. doi: 10.1098/rstb.2008.0218
11	周宇, 张慧, 冷沙. 战场上的"伪装者"——伪装技术对作战的影响[J]. 军事文摘, 2020, (7): 16- 20.
	ZHOU Y , ZHANG H , LENG S . "Pretenders" on the battlefield—the influence of camouflage technology on combat[J]. Military Digest, 2020, (7): 16- 20.
12	鲜晓东, 李克文. 基于颜色和纹理特征的伪装色矿工目标检测[J]. 计算机应用, 2013, 33 (2): 539- 542.
	XIAN X D , LI K W . Camouflage miner target detection based on color and texture features[J]. Computer Applications, 2013, 33 (2): 539- 542.
13	FAN D P, JI G P, ZHOU T, et al. Pranet: parallel reverse attention network for polyp segmentation[C]//Proc. of the International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020: 263-273.
14	SENGOTTUVELAN P, WAHI A, SHANMUGAM A. Performance of decamouflaging through exploratory image analysis[C]//Proc. of the IEEE International Conference on Emerging Trends in Engineering and Technology, 2008: 6-10.
15	PAN Y X , CHEN Y W , FU Q , et al. Study on the camouflaged target detection method based on 3D convexity[J]. Modern Applied Science, 2011, 5 (4): 152- 157.
16	CHEN Q, WANG Y M, YANG T, et al. You only look one-level feature[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13039-13048.
17	SUN P, ZHANG W, WANG H, et al. Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 1407-1417.
18	BOUDIAF M, KERVADEC H, MASUD Z I, et al. Few-shot segmentation without meta-learning: a good transductive inference is all you need?[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13979-13988.
19	LV Y Q, ZHANG J, DAI Y C, et al. Simultaneously localize, segment and rank the camouflaged objects[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 11591-11601.
20	HALL J R, CUTHILL I C, BADDELEY R, et al. Camouflage, detection and identification of moving targets[J]. Proceedings of the Royal Society B: Biological Sciences, 2013, 280(1758): 20130064.
21	金国栋, 薛远亮, 谭力宁, 等. 基于孪生神经网络的目标跟踪算法进展研究[J]. 系统工程与电子技术, 2022, 44 (6): 1805- 1822.
	JIN G D , XUE Y L , TAN L N , et al. Research on the progress of target tracking algorithm based on twin neural network[J]. Systems Engineering and Electronics, 2022, 44 (6): 1805- 1822.
22	曹旭, 邹焕新, 成飞, 等. 基于RHTC网络的飞机目标检测与精细识别[J]. 系统工程与电子技术, 2021, 43 (12): 3439- 3451. doi: 10.12305/j.issn.1001-506X.2021.12.04
	CAO X , ZOU H X , CHENG F , et al. Aircraft target detection and fine recognition based on RHTC network[J]. Systems Engineering and Electronics, 2021, 43 (12): 3439- 3451. doi: 10.12305/j.issn.1001-506X.2021.12.04
23	LIU Y D, WANG Y T, WANG S W, et al. Cbnet: a novel composite backbone network architecture for object detection[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2020: 11653-11660.
24	DING X H, ZHANG X Y, HAN J G, et al. Diverse branch block: building a convolution as an inception-like unit[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 10886-10895.
25	LEE Y W, PARK J. Centermask: real-time anchor-free instance segmentation[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 13906-13915.
26	WANG X, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7794-7803.
27	DE BOER P T , KROESE D P , MANNOR S , et al. A tutorial on the cross-entropy method[J]. Annals of Operations Research, 2005, 134 (1): 19- 67. doi: 10.1007/s10479-005-5724-z
28	QIN X B, ZHANG Z C, HUANG C Y, et al. Basnet: boundary-aware salient object detection[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 7479-7489.
29	WANG Z, SIMONCELLI E P, BOVIK A C. Multi-scale structural similarity for image quality assessment[C]//Proc. of the IEEE 37th Asilomar Conference on Signals, Systems & Computers, 2003: 1398-1402.
30	FAN D P, GONG C, CAO Y, et al. Enhanced-alignment measure for binary foreground map evaluation[C]//Proc. of the 27th International Joint Conference on Artificial Intelligence, 2018: 698-703.
31	FAN D P, CHENG M M, LIU Y, et al. Structure-measure: a new way to evaluate foreground maps[C]//Proc. of the IEEE International Conference on Computer Vision, 2017: 4548-4557.
32	MARGOLIN R, ZELNIK-MANOR L, TAL A. How to evaluate foreground maps?[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 248-255.
33	PERAZZI F, KRÄHENBVHL P, PRITCH Y, et al. Saliency filters: contrast based filtering for salient region detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2012: 733-740.
34	ZHAO J X, LIU J J, FAN D P, et al. EGNet: edge guidance network for salient object detection[C]//Proc. of the IEEE/CVF International Conference on Computer Vision, 2019: 8779-8788.
35	WANG B, CHEN Q, ZHOU M, et al. Progressive feature polishing network for salient object detection[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2020: 12128-12135.
36	QIN X B , ZHANG Z C , HUANG C Y , et al. U2-net: going deeper with nested ustructure for salient object detection[J]. Pattern Recognition, 2020, 106, 107404. doi: 10.1016/j.patcog.2020.107404
37	PANG Y W, ZHAO X Q, ZHANG L H, et al. Multi-scale interactive network for salient object detection[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 9413-9422.
38	WEI J, WANG S H, HUANG Q M. F3Net: fusion, feedback and focus for salient object detection[C]//Proc. of the AAAI Conference on Artificial Intelligence, 2020: 12321-12328.

方法	发布年份	COD10K-Test(2026张）				CAMO-Test(250张）				CPD1K-Test(200张）
方法	发布年份	E_ϕ↑	S_α↑	F_β^ω↑	M↓	E_ϕ↑	S_α↑	F_β^ω↑	M↓	E_ϕ↑	S_α↑	F_β^ω↑	M↓
EGNe^[34]	2019 ICCV	0.780	0.735	0.504	0.052	0.769	0.736	0.587	0.106	0.505	0.589	0.248	0.018
PFPN^[35]	2020 AAAI	0.779	0.745	0.484	0.062	0.778	0.736	0.572	0.116	0.791	0.786	0.434	0.016
U-2-net^[36]	2020 PR	0.737	0.708	0.482	0.064	0.645	0.634	0.447	0.129	0.904	0.881	0.749	0.005
MINet^[37]	2020 CVPR	0.837	0.767	0.600	0.048	0.802	0.747	0.635	0.095	0.881	0.851	0.704	0.005
F3Net^[38]	2020 AAAI	0.831	0.788	0.620	0.046	0.846	0.784	0.672	0.088	0.917	0.883	0.774	0.005
SINet^[4]	2020 CVPR	0.804	0.770	0.550	0.053	0.769	0.749	0.604	0.102	0.866	0.846	0.589	0.011
平均值		0.795	0.752	0.540	0.054	0.768	0.731	0.586	0.106	0.811	0.806	0.583	0.010
本文（Ⅰ）		0.873	0.801	0.660	0.039	0.859	0.785	0.697	0.083	0.950	0.889	0.794	0.004
本文（Ⅱ)		0.808	0.719	0.595	0.105	0.808	0.719	0.595	0.105	0.758	0.743	0.489	0.014

训练方案	消融对照			COD10K-test
训练方案	DB	LM双注意力	ACM补偿	E_ϕ↑	S_α↑	F_β^ω↑	M↓
方案Ⅰ	√	√	√	0.873	0.801	0.660	0.039
方案Ⅰ		√	√	0.859	0.757	0.623	0.043
方案Ⅰ	√		√	0.843	0.740	0.617	0.048
方案Ⅰ	√	√	√	0.856	0.753	0.633	0.053
方案Ⅰ	√	√		0.797	0.708	0.597	0.121
COD10K	√	√	√	0.808	0.719	0.595	0.105
CAMO	√	√	√	0.784	0.692	0.566	0.125
CPD1K	√	√	√	0.742	0.634	0.513	0.184

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A method of camouflaged object segmentation with locating and asymmetric compensation

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