# Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

arXiv: https://arxiv.org/abs/2111.03216 · HTML: https://mlsanity.com/paper/2111.03216/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 94 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 30 of 48)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [Mask Scoring R-CNN](https://mlsanity.com/paper/1903.00241/) (2019)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [Anabranch Network for Camouflaged Object Segmentation](https://mlsanity.com/paper/2105.09451/) (2021)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Simultaneously Localize, Segment and Rank the Camouflaged Objects](https://mlsanity.com/paper/2103.04011/) (2021)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)

## Built on by (most cited first, 30 of 42)

- [Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection](https://mlsanity.com/paper/2304.04709/) (2023)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Deep Gradient Learning for Efficient Camouflaged Object Detection](https://mlsanity.com/paper/2205.12853/) (2022)
- [CamoFormer: Masked Separable Attention for Camouflaged Object Detection](https://mlsanity.com/paper/2212.06570/) (2022)
- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [Feature Aggregation and Propagation Network for Camouflaged Object Detection](https://mlsanity.com/paper/2212.00990/) (2022)
- [A Survey of Camouflaged Object Detection and Beyond](https://mlsanity.com/paper/2408.14562/) (2024)
- [Camouflaged Object Detection via Context-aware Cross-level Fusion](https://mlsanity.com/paper/2207.13362/) (2022)
- [Effectiveness Assessment of Recent Large Vision-Language Models](https://mlsanity.com/paper/2403.04306/) (2024)
- [Diffusion Model for Camouflaged Object Detection](https://mlsanity.com/paper/2308.00303/) (2023)
- [Boosting Camouflaged Object Detection with Dual-Task Interactive Transformer](https://mlsanity.com/paper/2205.10579/) (2022)
- [DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation](https://mlsanity.com/paper/2212.11677/) (2022)
- [Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object Detection](https://mlsanity.com/paper/2311.11273/) (2023)
- [Towards Deeper Understanding of Camouflaged Object Detection](https://mlsanity.com/paper/2205.11333/) (2022)
- [Referring Camouflaged Object Detection](https://mlsanity.com/paper/2306.07532/) (2023)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [Frequency-Guided Spatial Adaptation for Camouflaged Object Detection](https://mlsanity.com/paper/2409.12421/) (2024)
- [Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well](https://mlsanity.com/paper/2502.14471/) (2025)
- [Evaluating SAM2's Role in Camouflaged Object Detection: From SAM to SAM2](https://mlsanity.com/paper/2407.21596/) (2024)
- [GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection](https://mlsanity.com/paper/2409.09588/) (2024)
- [Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark](https://mlsanity.com/paper/2310.04253/) (2023)
- [Large Model Based Referring Camouflaged Object Detection](https://mlsanity.com/paper/2311.17122/) (2023)
- [PrObeD: Proactive Object Detection Wrapper](https://mlsanity.com/paper/2310.18788/) (2023)
- [COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection](https://mlsanity.com/paper/2411.18858/) (2024)
- [Deep Learning in Concealed Dense Prediction](https://mlsanity.com/paper/2504.10979/) (2025)
- [PlantCamo: Plant Camouflage Detection](https://mlsanity.com/paper/2410.17598/) (2024)
- [Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection](https://mlsanity.com/paper/2307.10685/) (2023)
- [Adaptive Guidance Learning for Camouflaged Object Detection](https://mlsanity.com/paper/2405.02824/) (2024)
- [Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning](https://mlsanity.com/paper/2307.04651/) (2023)
- [You Do Not Need Additional Priors in Camouflage Object Detection](https://mlsanity.com/paper/2310.00702/) (2023)

## Abstract (arXiv)

Camouflaged Object Detection (COD) aims to detect objects with similar patterns (e.g., texture, intensity, colour, etc) to their surroundings, and recently has attracted growing research interest. As camouflaged objects often present very ambiguous boundaries, how to determine object locations as well as their weak boundaries is challenging and also the key to this task. Inspired by the biological visual perception process when a human observer discovers camouflaged objects, this paper proposes a novel edge-based reversible re-calibration network called ERRNet. Our model is characterized by two innovative designs, namely Selective Edge Aggregation (SEA) and Reversible Re-calibration Unit (RRU), which aim to model the visual perception behaviour and achieve effective edge prior and cross-comparison between potential camouflaged regions and background. More importantly, RRU incorporates diverse priors with more comprehensive information comparing to existing COD models. Experimental results show that ERRNet outperforms existing cutting-edge baselines on three COD datasets and five medical image segmentation datasets. Especially, compared with the existing top-1 model SINet, ERRNet significantly improves the performance by ~6% (mean E-measure) with notably high speed (79.3 FPS), showing that ERRNet could be a general and robust solution for the COD task.