Abstract Camouflaged object detection(COD) aims to segment target objects that are visually highly integrated into their surrounding environments. However, a large number of similar interferences between the foreground and background of the object lead to significant segmentation errors in the process. To address this issue, dynamic supervised camouflaged object detection network with semantic reconstruction(DSSRNet) is proposed to achieve accurate segmentation of camouflaged objects by reconstructing the spatial semantics of the feature map and introducing confidence to guide network training. Firstly, a spatial semantic low-rank reconstruction mechanism is proposed to effectively perceive distinguishable semantic features of camouflaged objects at different scales. Secondly, the COD network is dynamically supervised by generating confidence prediction maps to minimize false positive and false negative judgments due to the overconfidence in the network. Finally, the blurred awareness loss function is employed to reduce the ambiguity of the prediction. Experiments on CAMO-Test, COD10K-Test and NC4K datasets demonstrate that DSSRNet provides better exclusion of interference and achieves more accurate segmentation results.
Fund:National Natural Science Foundation of China(No.61172144), Natural Science Foundation of Liaoning Province(No.20170540426), Key Project of Education Department of Liaoning Province(No.LJYL049)
Corresponding Authors:
JIANG Wentao, Ph.D., associate professor. His research interests include image processing, pattern re-cognition and artificial intelligence.
About author:: WANG Bohan, Master student. His research interests include image processing, pattern recognition and artificial intelligence.
[1] FAN D P, JI G P, SUN G L, et al. Camouflaged Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2020: 2774-2784. [2] BARBOSA A, MÄTHGER L M, BURESCH K C, et al. Cuttlefish Camouflage: The Effects of Substrate Contrast and Size in Evoking Uniform, Mottle or Disruptive Body Patterns. Vision Research, 2008, 48(10): 1242-1253. [3] FAN D P, JI G P, ZHOU T, et al. PraNet: Parallel Reverse Attention Network for Polyp Segmentation // Proc of the Conference on Medical Image Computing and Computer Assisted Intervention. Berlin, Germany: Springer, 2020: 263-273. [4] FAN D P, ZHOU T, JI G P, et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging, 2020, 39(8): 2626-2637. [5] QIU L T, WU X J, YU Z Y.A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection. IEEE Access, 2019, 7: 15884-15893. [6] AMIT S N K B, SHIRAISHI S, INOSHITA T, et al. Analysis of Satellite Images for Disaster Detection // Proc of the IEEE International Geoscience and Remote Sensing Symposium. Washington, USA: IEEE, 2016: 5189-5192. [7] YI D W, SU J Y, CHEN W H.Locust Recognition and Detection via Aggregate Channel Features // Proc of the UK-RAS Conference on Robotics and Autonomous Systems. New York, USA: ACM, 2019: 112-115. [8] PAN Y X, CHEN Y W, FU Q, et al. Study on the Camouflaged Target Detection Method Based on 3D Convexity. Modern Applied Science, 2011, 5(4): 152-157. [9] GE S M, JIN X, YE Q T, et al. Image Editing by Object-Aware Optimal Boundary Searching and Mixed-Domain Composition. Computational Visual Media, 2018, 4(1): 71-82. [10] SUN Y J, CHEN G, ZHOU T, et al. Context-Aware Cross-Level Fusion Network for Camouflaged Object Detection // Proc of the 30th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2021: 1025-1031. [11] SUN Y J, WANG S, CHEN C L Z, et al. Boundary-Guided Camouflaged Object Detection // Proc of the 31st International Joint Conference on Artificial Intelligence Main Track. San Francisco, USA: IJCAI, 2022: 1335-1341. [12] LÜ Y, ZHANG J, DAI Y C, et al. Simultaneously Localize, Segment and Rank the Camouflaged Objects // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 11586-11596. [13] LE T N, NGUYEN T V, NIE Z L, et al. Anabranch Network for Camouflaged Object Segmentation. Computer Vision and Image Understanding, 2019, 184: 45-56. [14] ZHAI Q, LI X, YANG F, et al. Mutual Graph Learning for Camouflaged Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 12992-13002. [15] JIA Q, YAO S L, LIU Y, et al. Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 4703-4712. [16] JI G P, FAN D P, CHOU Y C, et al. Deep Gradient Learning for Efficient Camouflaged Object Detection. Machine Intelligence Research, 2023, 20: 92-108. [17] MEI H Y, JI G P, WEI Z Q, et al. Camouflaged Object Segmentation with Distraction Mining // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 8768-8777. [18] JI G P, ZHU L, ZHUGE M C, et al. Fast Camouflaged Object Detection via Edge-Based Reversible Re-calibration Network. Pattern Recognition, 2022, 123. DOI: 10.1016/j.patcog.2021.108414. [19] REN J J, HU X W, ZHU L, et al. Deep Texture-Aware Features for Camouflaged Object Detection. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(3): 1157-1167. [20] YANG F, ZHAI Q, LI X, et al. Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2021: 4126-4135. [21] ZHONG Y J, LI B, TANG L, et al. Detecting Camouflaged Object in Frequency Domain // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 4494-4503. [22] LIU W H, SHEN X, PUN C M, et al. Explicit Visual Prompting for Low-Level Structure Segmentations // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2023: 19434-19445 [23] KENDALL A, GAL Y R.What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision // Proc of the 31st International Conference on Neural Information Processing Systems. Cambridge,USA: MIT Press, 2017: 5580-5590. [24] SENSOY M, KAPLAN L, KANDEMIR M.Evidential Deep Lear-ning to Quantify Classification Uncertainty // Proc of the 32nd International Conference on Neural Information Processing Systems. Cambridge,USA: MIT Press, 2018: 3183-3193. [25] KONG L K, SUN J M, ZHANG C.SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates // Proc of the 37th International Conference on Machine Learning. New York, USA: ACM, 2020: 5405-5415. [26] WANNENWETSCH A S, ROTH S.Probabilistic Pixel-Adaptive Refinement Networks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 11639-11648. [27] MA W L, KARAKUŞ O, ROSIN P L.Confidence Guided Semi-supervised Learning in Land Cover Classification // Proc of the IEEE International Geoscience and Remote Sensing Symposium. Washington, USA: IEEE, 2023: 5487-5490. [28] LIU J W, ZHANG J, BARNES N.Confidence-Aware Learning for Camouflaged Object Detection[C/OL]. [2024-04-23].https://arxiv.org/abs/2106.11641. [29] PANG Y W, ZHAO X Q, XIANG T Z, et al. Zoom in and out: A Mixed-Scale Triplet Network for Camouflaged Object Detection // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 2150-2160. [30] HE C M, LI K, ZHANG Y C, et al. Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2023: 22046-22055. [31] HU X B, WANG S, QIN X B, et al. High-Resolution Iterative Feedback Network for Camouflaged Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(1): 881-889. [32] 童旭巍,张光建.基于全局多尺度特征融合的伪装目标检测网络.模式识别与人工智能, 2022, 35(12): 1122-1130. (TONG X W, ZHANG G J.Camouflaged Object Detection Network Based on Global Multi-scale Feature Fusion. Pattern Recognition and Artificial Intelligence, 2022,35(12): 1122-1130.) [33] 陈鹏,李旭,向道岸,等.边缘-分割交叉引导的伪装目标检测网络[J/OL]. [2024-04-23]. https://link.cnki.net/urlid/11.5602.TP.20240516.1528.002. (CHEN P, LI X, XIANG D A, et al. Edge-Segmentation Cross-guided Camouflage Object Detection Network[J/OL]. [2024-04-23]. https://link.cnki.net/urlid/11.5602.TP.20240516.1528.002.) [34] GAO S H, CHENG M M, ZHAO K, et al. Res2Net: A New Multi-scale Backbone Architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(2): 652-662. [35] WEI J, WANG S H, HUANG Q M.F3Net: Fusion, Feedback and Focus for Salient Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12321-12328. [36] RONNEBERGER O, FISCHER P, BROX T.U-Net: Convolutio-nal Networks for Biomedical Image Segmentation // Proc of the In-ternational Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2015: 234-241. [37] HE K M, ZHANG X Y, REN S Q, et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 1026-1034. [38] PREZZI F, ÄHENBÜHL P, PRITCH Y, et al. Saliency Filters: Contrast Based Filtering for Salient Region Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2012: 733-740. [39] FAN D P, GONG C, CAO Y, et al. Enhanced-Alignment Measure for Binary Foreground Map Evaluation // Proc of the 27th International Joint Conference on Artificial Intelligence Main Track. San Francisco, USA: IJCAI, 2018: 698-704. [40] FAN D P, CHENG M M, LIU Y, et al. Structure-Measure: A New Way to Evaluate Foreground Maps // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2017: 4558-4567. [41] MARGOLIN R, ZELNIK-MANOR L, TAL A.How to Evaluate Foreground Maps? // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 248-255. [42] LI J P, HE W, ZHANG H Y, et al. Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation[C/OL].[2024-04-23]. https://arxiv.org/pdf/2210.15156. [43] ZHAO J X, LIU J J, FAN D P, et al. EGNet: Edge Guidance Network for Salient Object Detection // Proc of the International Conference on IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 8778-8787.
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