Deep Gradient Learning for Efficient Camouflaged Object Detection (2205.12853v2)

Abstract: This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR$_{21}$ with only 6.82% parameters. Application results also show that the proposed DGNet performs well in polyp segmentation, defect detection, and transparent object segmentation tasks. Codes will be made available at https://github.com/GewelsJI/DGNet.

• The paper introduces DGNet, a novel framework using deep gradient learning and a gradient-induced transition for efficient camouflaged object detection.
• DGNet-S, a streamlined version, achieves real-time performance (80 FPS) and state-of-the-art results with significantly fewer parameters than comparable models.
• Beyond camouflaged object detection, DGNet shows versatility in applications like polyp segmentation, defect detection, and transparent object segmentation.

Deep Gradient Learning for Efficient Camouflaged Object Detection

The paper "Deep Gradient Learning for Efficient Camouflaged Object Detection" introduces DGNet, an innovative framework aimed at enhancing the detection of camouflaged objects (COD). The research addresses a critical challenge in computer vision where objects blend seamlessly into their surroundings, posing significant difficulties for automatic detection systems. This novel approach is grounded in the supervision of object gradients, facilitating more precise identification and segmentation of concealed entities.

Framework Overview

DGNet segregates the COD task into two complementary branches: a context encoder and a texture encoder. This separation hinges on the gradient-induced transition mechanism, which serves as a conduit interlinking the two branches. The transition relies on a soft grouping strategy that combines context and texture features, effectively reducing noise and enhancing edge definition in feature maps. This methodological innovation allows DGNet to significantly outpace existing models in both accuracy and computational efficiency.

Methodological Contributions

1. Gradient-Induced Transition (GIT): The introduction of GIT marks a pivotal component of DGNet. This module supports interactions between context and texture encoders by performing a multi-source aggregation at different group scales. It is designed to leverage intensity variations within the object and is instrumental in refining feature maps while minimizing background noise.
2. Efficient Model Design: DGNet-S, the streamlined version of the proposed network, is capable of real-time processing at 80 frames per second, offering comparable results to state-of-the-art models with a significantly lower parameter count, precisely 6.82% of the parameters used by JCSOD-CVPR$_{21}$.
3. Applications and Versatility: Beyond COD, DGNet has demonstrated its efficacy in various applications such as polyp segmentation, defect detection, and transparent object segmentation. This versatility underscores its potential for broader use in medical imaging and industrial inspection.

Experimental Results

DGNet undergoes rigorous evaluation across three challenging benchmarks, consistently achieving state-of-the-art performance. Notably, it surpasses existing models by a considerable margin in metrics like mean absolute error and structure measure. The empirical evidence suggests DGNet's superior capacity to detect and segment objects that are intrinsically challenging due to their camouflaged nature.

Implications and Future Directions

This research has significant implications for advancing automated detection systems in real-time applications. DGNet's architecture, particularly its ability to operate efficiently with limited computational resources, broadens the scope for its deployment in embedded systems and resource-constrained environments.

Future work could explore the integration of more sophisticated attention mechanisms and transformer models to further enhance feature representation and aggregation. Additionally, expanding the application of DGNet to more diverse datasets and domains could reveal further potential enhancements to its architecture.

In summary, DGNet offers a robust and efficient solution to camouflaged object detection, providing a valuable tool for both theoretical exploration and practical application in computer vision. Its strategic use of gradient-based learning sets it apart as a noteworthy contribution to the field.