Multi-scale Camouflaged Object Detection Method Based on Regional Feature Enhancement

doi:10.19678/j.issn.1000-3428.0067588

Abstract

Abstract: Camouflaged Object Detection(COD) can detect camouflaged objects with high similarity to the background in complex environments and is particularly important in military investigation and industrial detection. To address the low utilization of region-level feature information presented by existing COD methods, a multiscale COD method based on a Regional Feature Enhancement Network(RFE-Net) is proposed, which can accurately detect camouflaged objects under visible-light conditions. The RFE-Net includes a weak-semantic-feature enhancement module, spatial-information interaction module, and context-information aggregation module. First, the weak-semantic-feature enhancement module introduces strip pooling and asymmetric convolution to dynamically adjust the search regions by optimizing the receptive field of the network, thereby strengthening the connection between long-range weak semantic features. Subsequently, the cascading U-shaped block structure is combined into a spatial-information interaction module, which eliminates the interference of erroneous prediction samples. Finally, a context-information aggregation module is designed, which significantly improves the prediction accuracy by fully fusing deep semantic information and shallow fine-grained information to refine the object edge details. Experimental results show that the proposed method can strengthen weak semantic associations within an object and improve the distinction between camouflaged objects and the background. On the largest test set, NC4K, the proposed method achieves optimal values for four metrics: structural measure, enhanced alignment measure, weighted F1 value, and mean absolute error. In particular, the structural measure and mean absolute error are 1.1% and 7.7% higher than those of another method, respectively.

Key words: deep learning, Camouflaged Object Detection(COD), multi-scale fusion, feature enhancement, regional features

摘要： 伪装目标检测(COD)能够在复杂环境下探测出与背景相似度极高的伪装目标,在军事侦查和工业检测等领域具有重要的应用价值。针对现有伪装目标检测方法对区域级特征信息利用率低的问题,提出一种基于区域特征强化的多尺度伪装目标检测网络(RFE-Net)方法,实现可见光条件下伪装目标的准确探测。RFE-Net主要包含弱语义特征增强模块、空间信息交互模块和上下文信息聚合模块。首先弱语义特征增强模块引入了条状池化和非对称卷积,通过优化网络的感受野来动态调整搜索区域,从而加强长距离弱语义特征间的联系;然后将级联的U型块结构组合为空间信息交互模块,消除错误预测样本的干扰;最后设计上下文信息聚合模块,通过充分融合深层语义信息和浅层细粒度信息以精细化处理目标边缘细节,从而提升预测准确度。实验结果表明,所提方法能够加强目标内部的弱语义关联,提高目标与背景的区分度,在最大测试集NC4K上的结构性度量、增强对准度量、加权F1值和平均绝对误差4个指标上均取得最优值,其中结构性度量和平均绝对误差高于第2名方法1.1%和7.7%。

关键词: 深度学习, 伪装目标检测, 多尺度融合, 特征强化, 区域级特征

CLC Number:

TP391

SUN Bangyong, MA Ming, YU Tao. Multi-scale Camouflaged Object Detection Method Based on Regional Feature Enhancement[J]. Computer Engineering, 2024, 50(5): 209-219.

孙帮勇, 马铭, 于涛. 基于区域特征强化的多尺度伪装目标检测方法[J]. 计算机工程, 2024, 50(5): 209-219.

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References

[1] WEBSTER R J. Does disruptive camouflage conceal edges and features?[J]. Current Zoology, 2015, 61(4):708-717.
[2] FAN D P, JI G P, ZHOU T, et al. PraNet:parallel reverse attention network for polyp segmentation[C]//Proceedings of Medical Image Computing and Computer Assisted Intervention. New York, USA:ACM Press, 2020:263-273.
[3] UCAR F, KORKMAZ D. COVIDiagnosis-Net:deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 from X-ray images[J]. Medical Hypotheses, 2020, 140:109761.
[4] FUENTE R, DELCLÒS X, PEÑALVER E, et al. Early evolution and ecology of camouflage in insects[J]. Proceedings of National Academy of Sciences of the United States of America, 2012, 109(52):21414-21419.
[5] LIU Y, WANG C Q, ZHOU Y J. Camouflaged people detection based on a semi-supervised search identification network[J]. Defence Technology, 2023, 21:176-183.
[6] 蒋昕昊, 蔡伟, 张志利, 等. 基于COSNet的伪装目标分割[J]. 兵工学报, 2023, 44(5):1456-1468. JIANG X H, CAI W, ZHANG Z L, et al. Camouflaged object segmentation based on COSNet[J]. Acta Armamentarii, 2023, 44(5):1456-1468. (in Chinese)
[7] SENGOTTUVELAN P, WAHI A, SHANMUGAM A. Performance of decamouflaging through exploratory image analysis[C]//Proceedings of the 1st International Conference on Emerging Trends in Engineering and Technology. Washington D. C.,USA:IEEE Press, 2008:6-10.
[8] SIRICHAROEN P, ARAMVITH S, CHALIDABHONGSE T H, et al. Robust outdoor human segmentation based on color-based statistical approach and edge combination[C]//Proceedings of 2010 International Conference on Green Circuits and Systems. Washington D. C.,USA:IEEE Press, 2010:463-468.
[9] HOU W D, LI J P, YIN J Q, et al. Detection of the mobile object with camouflage color under dynamic background based on optical flow[J]. Procedia Engineering, 2011, 15:2201-2205.
[10] HOU Q B, ZHANG L, CHENG M M, et al. Strip pooling:rethinking spatial pooling for scene parsing[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA:IEEE Press, 2020:4002-4011.
[11] YAN J N, LE T N, NGUYEN K D, et al. MirrorNet:bioinspired camouflaged object segmentation[J]. IEEE Access, 2021, 9:43290-43300.
[12] LV Y Q, ZHANG J, DAI Y C, et al. Simultaneously localize, segment and rank the camouflaged objects[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2021:11586-11596.
[13] CHEN T Y, XIAO J, HU X G, et al. Boundary-guided network for camouflaged object detection[J]. Knowledge-Based Systems, 2022, 248:108901.
[14] SUN Y J, CHEN G, ZHOU T, et al. Context-aware cross-level fusion network for camouflaged object detection[C]//Proceedings of the 30th International Joint Conference on Artificial Intelligence. Washington D. C.,USA:IEEE Press, 2021:1025-1031.
[15] DONG B, ZHUGE M C, WANG Y X, et al. Accurate camouflaged object detection via mixture convolution and interactive fusion[EB/OL].[2021-04-01]. https://arxiv.org/pdf/2101.05687v2.pdf.
[16] PANG Y W, ZHAO X Q, XIANG T Z, et al. Zoom in and out:a mixed-scale triplet network for camouflaged object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2022:2160-2170.
[17] HU X B, FAN D P, QIN X B, et al. High-resolution iterative feedback network for camouflaged object detection[EB/OL].[2022-04-01]. https://arxiv.org/pdf/2203.11624.pdf.
[18] FAN D P, JI G P, SUN G L, et al. Camouflaged object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA:IEEE Press, 2020:2774-2784.
[19] MEI H Y, JI G P, WEI Z Q, et al. Camouflaged object segmentation with distraction mining[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2021:8768-8777.
[20] ZHAI Q, LI X, YANG F, et al. Mutual graph learning for camouflaged object detection[C]//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2021:12992-13002.
[21] YANG F, ZHAI Q, LI X, et al. Uncertainty-guided transformer reasoning for camouflaged object detection[C]//Proceedings of IEEE/CVF International Conference on Computer Vision. Washington D. C.,USA:IEEE Press, 2021:4126-4135.
[22] JI G P, FAN D P, CHOU Y C, et al. Deep gradient learning for efficient camouflaged object detection[J]. Machine Intelligence Research, 2023, 20(1):92-108.
[23] 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.
[24] MATTYUS G, LUO W J, URTASUN R. DeepRoadMapper:extracting road topology from aerial images[C]//Proceedings of IEEE International Conference on Computer Vision. Washington D. C.,USA:IEEE Press, 2017:3458-3466.
[25] WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale structural similarity for image quality assessment[C]//Proceedings of the 37th Asilomar Conference on Signals, Systems & Computers. Washington D. C.,USA:IEEE Press, 2003:1398-1402.
[26] LE T N, CAO Y B, NGUYEN T C, et al. Camouflaged instance segmentation In-the-wild:dataset, method, and benchmark suite[J]. IEEE Transactions on Image Processing, 2022, 31:287-300.
[27] TAN M X, LE Q V. EfficientNet:rethinking model scaling for convolutional neural networks[C]//Proceedings of the 36th International Conference on Machine Learning. Washington D. C.,USA:IEEE Press, 2019:6105-6114.
[28] FAN D P, CHENG M M, LIU Y, et al. Structure-measure:a new way to evaluate foreground maps[C]//Proceedings of IEEE International Conference on Computer Vision. Washington D. C.,USA:IEEE Press, 2017:4558-4567.
[29] FAN D P, GONG C, CAO Y, et al. Enhanced-alignment measure for binary foreground map evaluation[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. New York, USA:ACM Press, 2018:698-704.
[30] PERAZZI F, KRAHENBUHL P, PRITCH Y, et al. Saliency filters:contrast based filtering for salient region detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2012:733-740.
[31] MARGOLIN R, ZELNIK-MANOR L, TAL A. How to evaluate foreground maps[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2014:248-255.
[32] FAN D P, JI G P, CHENG M M, et al. Concealed object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10):6024-6042.
[33] WU Z, SU L, HUANG Q M. Cascaded partial decoder for fast and accurate salient object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2019:3902-3911.
[34] ZHAO J X, LIU J J, FAN D P, et al. EGNet:edge guidance network for salient object detection[C]//Proceedings of IEEE/CVF International Conference on Computer Vision. Washington D. C.,USA:IEEE Press, 2019:8778-8787.
[35] ZHANG J, FAN D P, DAI Y C, et al. UC-Net:uncertainty inspired RGB-D saliency detection via conditional variational autoencoders[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press,2020:8582-8591.
[36] LI A X, ZHANG J, LV Y Q, et al. Uncertainty-aware joint salient object and camouflaged object detection[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C.,USA:IEEE Press, 2021:10066-10076.
[37] REN J J, HU X W, ZHU L, et al. Deep texture-aware features for camouflaged object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(3):1157-1167.
[38] LIU Z Y, ZHANG Z L, TAN Y C, et al. Boosting camouflaged object detection with dual-task interactive transformer[C]//Proceedings of the 26th International Conference on Pattern Recognition. Washington D. C.,USA:IEEE Press, 2022:140-146.

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