Camouflage Target Detection Method with Mutual Compensation of Local-Global Features

doi:10.3778/j.issn.1673-9418.2311097

Abstract

Abstract: In the field of camouflage object detection (COD), the latest proposed methods mainly use the local features of the camouflaged target to complete the COD task. The output prediction map has problems such as rough target contours and incomplete objects. In response to the above problems, this paper proposes a camouflaged target detection method based on local-global feature mutual compensation, which uses local features and global features to compensate for each other to detect camouflaged targets. Firstly, a non-local feature enhancement module (N-LFEM) is designed to use a non-local mechanism to interact with adjacent local areas and enhance local feature expression. Then, a local-global feature interaction module (L-GFIM) is constructed to average local features to obtain global features, and perform mutual compensation of local features and global features. Finally, a local-global feature cross-covariance module (L-GFCCM) is designed to obtain spatial indicators through semantic alignment and cross-covariance to locate the area where the camouflaged target is located, and select the feature map with the highest similarity to output. Experiments on 3 public datasets show that this algorithm is better than the other 8 latest models. On the COD10K dataset, the mean absolute error (MAE) reaches 0.028.

Key words: camouflage object detection, local-global feature mutual compensation, local-global feature cross-covariance

摘要： 在伪装目标检测（COD）领域中，最新提出的方法主要利用伪装目标的局部特征完成COD任务，输出的预测图存在目标轮廓粗糙和对象不完整的问题。针对上述问题，提出了基于局部-全局特征相互补偿的伪装目标检测方法，利用局部特征与全局特征相互补偿进行伪装目标的检测。设计一个非局部特征增强模块（N-LFEM），使用非局部机制来交互相邻局部区域，增强局部特征表达。构建一个局部-全局特征交互模块（L-GFIM），平均局部特征得到全局特征，执行局部特征与全局特征的相互补偿。设计一个局部-全局特征交叉协方差模块（L-GFCCM），通过语义对齐和交叉协方差获取空间指标定位伪装目标所在区域，选取相似性最高的特征图输出。在3个公开数据集上的实验表明，该算法优于其他8个最新模型。在COD10K数据集上，平均绝对误差（MAE）达到了0.028。

关键词: 伪装目标检测, 局部-全局特征相互补偿, 局部-全局特征交叉协方差

HE Wenhao, GE Haibo. Camouflage Target Detection Method with Mutual Compensation of Local-Global Features[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(2): 454-464.

何文昊, 葛海波. 局部-全局特征相互补偿的伪装目标检测方法[J]. 计算机科学与探索, 2025, 19(2): 454-464.

References

[1] FAN D P, JI G P, SUN G L, et al. Camouflaged object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 2774-2784.
[2] NAFUS M G, GERMANO J M, PERRY J A, et al. Hiding in plain sight: a study on camouflage and habitat selection in a slow-moving desert herbivore[J]. Behavioral Ecology, 2015, 26(5): 1389-1394.
[3] CHEN G, LIU S J, SUN Y J, et al. Camouflaged object detection via context-aware cross-level fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(10): 6981-6993.
[4] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[EB/OL]. [2023-09-16]. https://arxiv.org/abs/2005.12872.
[5] TANKUS A, YESHURUN Y. Convexity-based visual camouflage breaking[J]. Computer Vision and Image Understanding, 2001, 82(3): 208-237.
[6] BHAJANTRI N U, NAGABHUSHAN P. Camouflage defect identification: a novel approach[C]//Proceedings of the 9th International Conference on Information Technology. Piscataway: IEEE, 2006: 145-148.
[7] XUE F, YONG C X, XU S, et al. Camouflage performance analysis and evaluation framework based on features fusion[J]. Multimedia Tools and Applications, 2016, 75(7): 4065-4082.
[8] PIKE T W. Quantifying camouflage and conspicuousness using visual salience[J]. Methods in Ecology and Evolution, 2018, 9(8): 1883-1895.
[9] SUN Y J, CHEN G, ZHOU T, et al. Context-aware cross-level fusion network for camouflaged object detection[EB/OL]. [2023-09-16]. https://arxiv.org/abs/2105.12555.
[10] LE T N, NGUYEN T V, NIE Z L, et al. Anabranch network for camouflaged object segmentation[J]. Computer Vision and Image Understanding, 2019, 184: 45-56.
[11] YAN J N, LE T N, NGUYEN K D, et al. MirrorNet: bio-inspired camouflaged object segmentation[J]. IEEE Access, 2021, 9: 43290-43300.
[12] ZHAI Q, LI X, YANG F, et al. Mutual graph learning for camouflaged object detection[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 12992-13002.
[13] 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, 2019, 33: 1157-1167.
[14] LI A X, ZHANG J, LV Y Q, et al. Uncertainty-aware joint salient object and camouflaged object detection[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 10066-10076.
[15] CHEN G, CHEN X R, DONG B, et al. Towards accurate camouflaged object detection with mixture convolution and interactive fusion[EB/OL]. [2023-09-16]. https://arxiv.org/abs/2101.05687.
[16] FAN D P, JI G P, SUN G L, et al. Camouflaged object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 2774-2784.
[17] MEI H Y, JI G P, WEI Z Q, et al. Camouflaged object segmentation with distraction mining[EB/OL]. [2023-09-16]. https://arxiv.org/abs/2104.10475.
[18] KIM J, PAVLOVIC V. A shape-based approach for salient object detection using deep learning[C]// Proceedings of the 14th European Conference on Computer Vision. Cham: Springer, 2016: 455-470.
[19] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015: 3431-3440.
[20] ZHUGE M C, FAN D P, LIU N, et al. Salient object detection via integrity learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(3): 3738-3752.
[21] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 936-944.
[22] FAN Q, FAN D P, FU H Z, et al. Group collaborative learning for co-salient object detection[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 12283-12293.
[23] CHEN S H, TAN X L, WANG B, et al. Reverse attention for salient object detection[C]//Proceedings of the 15th European Conference on Computer Vision. Cham: Springer, 2018: 236-252.
[24] CHANDRA S, USUNIER N, KOKKINOS I. Dense and low-rank Gaussian CRFs using deep embeddings[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 5113-5122.
[25] LI Y, GUPTA A. Beyond grids: learning graph representations for visual recognition[C]//Advances in Neural Information Processing Systems 31, Montréal,Dec 3-8, 2018: 9245-9255.
[26] LU Y, CHEN Y R, ZHAO D B, et al. Graph-FCN for image semantic segmentation[C]//Proceedings of the 2019 International Symposium on Neural Networks. Cham: Springer, 2019: 97-105.
[27] POURIAN N, KARTHIKEYAN S, MANJUNATH B S. Weakly supervised graph based semantic segmentation by learning communities of image-parts[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 1359-1367.
[28] ZHANG L, LI X T, ARNAB A, et al. Dual graph convolutional network for semantic segmentation[EB/OL]. [2023-09-16]. https://arxiv.org/abs/1909.06121.
[29] HOU R, CHANG H, MA B, et al. Cross attention network for few-shot classification[C]//Advances in Neural Information Processing Systems 32, Vancouver, Dec 8-14, 2019: 4003-4014.
[30] LIU C. Learning a few-shot embedding model with contrastive learning[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, the 33rd Conference on Innovative Applications of Artificial Intelligence, the 11th Symposium on Educational Advances in Artificial Intelligence.Menlo Park: AAAI, 2021: 8635-8643.
[31] SIMON C, KONIUSZ P, NOCK R, et al. Adaptive subspaces for few-shot learning[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 4135-4144.
[32] LIFCHITZ Y, AVRITHIS Y, PICARD S, et al. Dense classification and implanting for few-shot learning[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 9250- 9259.
[33] HARIHARAN B, GIRSHICK R. Low-shot visual recognition by shrinking and hallucinating features[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 3037-3046.
[34] ZHANG R, CHE T, GHAHRAMANI Z, et al. MetaGAN: an adversarial approach to few-shot learning[C]//Advances in Neural Information Processing Systems 31, Montréal,Dec 3-8, 2018: 2371-2380.
[35] LI K, ZHANG Y L, LI K P, et al. Adversarial feature hallucination networks for few-shot learning[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 13467-13476.
[36] SUNG F, YANG Y X, ZHANG L, et al. Learning to compare: relation network for few-shot learning[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 1199-1208.
[37] ZHANG X T, QIANG Y T, SUNG F, et al. RelationNet2: deep comparison columns for few-shot learning[EB/OL]. [2023-09-16]. https://arxiv.org/abs/1811.07100.
[38] HAO F S, HE F X, CHENG J, et al. Collect and select: semantic alignment metric learning for few-shot learning[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 8459-8468.
[39] ZHANG C, CAI Y J, LIN G S, et al. DeepEMD: few-shot image classification with differentiable earth mover’s distance and structured classifiers[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 12200-12210.
[40] WANG X L, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 7794-7803.
[41] TE G S, LIU Y L, HU W, et al. Edge-aware graph representation learning and reasoning for face parsing[C]//Proceedings of the 16th European Conference on Computer Vision. Cham: Springer, 2020: 258-274.
[42] HE K M, FAN H Q, WU Y X, et al. Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 9726- 9735.
[43] LV Y Q, ZHANG J, DAI Y C, et al. Simultaneously localize, segment and rank the camouflaged objects[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 11586- 11596.
[44] PERAZZI F, KRÄHENBÜHL P, PRITCH Y, et al. Saliency filters: contrast based filtering for salient region detection[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2012: 733-740.
[45] FAN D P, JI G P, QIN X, et al. Cognitive vision inspired object segmentation metric and loss function[J]. Scientia Sinica Informationis, 2021, 51(9): 1475.
[46] FAN D P, CHENG M M, LIU Y, et al. Structure-measure: a new way to evaluate foreground maps[EB/OL]. [2023-09-16]. https://arxiv.org/abs/1708.00786.
[47] MARGOLIN R, ZELNIK-MANOR L, TAL A. How to evaluate foreground maps[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2014: 248-255.
[48] ZHANG D W, ZHENG Z L, LI M L, et al. CSART: channel and spatial attention-guided residual learning for real-time object tracking[J]. Neurocomputing, 2021, 436: 260-272.