Confidence-Aware Learning for Camouflaged Object Detection

Liu, Jiawei; Zhang, Jing; Barnes, Nick

doi:10.48550/arxiv.2106.11641

Cited by 1 publication

(1 citation statement)

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“…Fan et al [22] first released a relatively complete camouflaged object dataset, COD10K, and proposed SINet based on a cascade partial decoder [23]. Some studies [6,24] also focus on extracting the edges around camouflaged objects more accurately. Furthermore, some research adopts a multi-task learning framework, introducing tasks such as object ordering [5] or edge detection [25] to facilitate robust camouflaged object segmentation.…”

Section: Related Workmentioning

confidence: 99%

Robust Localization-Guided Dual-Branch Network for Camouflaged Object Segmentation

Wang,

Li,

Wei

et al. 2024

Electronics

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The existence of camouflage targets is widespread in the natural world, as they blend seamlessly or closely resemble their surrounding environment, making it difficult for the human eye to identify them accurately. In camouflage target segmentation, challenges often arise from the high similarity between the foreground and background, resulting in segmentation errors, imprecise edge detection, and overlooking of small targets. To address these issues, this paper presents a robust localization-guided dual-branch network for the recognition of camouflaged targets. Two crucial branches, i.e., a localization branch and an overall refinement branch are designed and incorporated. The localization branch achieves accurate preliminary localization of camouflaged targets by incorporating the robust localization module, which integrates different high-level feature maps in a partially decoded manner. The overall refinement branch optimizes segmentation accuracy based on the output predictions of the localization branch. Within this branch, the edge refinement module is devised to effectively reduce false negative and false positive interference. By conducting context exploration on each feature layer from top to bottom, this module further enhances the precision of target edge segmentation. Additionally, our network employs five jointly trained output prediction maps and introduces attention-guided heads for diverse prediction maps in the overall refinement branch. This design adjusts the spatial positions and channel weights of different prediction maps, generating output prediction maps based on the emphasis of each output, thereby further strengthening the perception and feature representation capabilities of the model. To improve its ability to generate highly confident and accurate prediction candidate regions, tailored loss functions are designed to cater to the objectives of different prediction maps. We conducted experiments on three publicly available datasets for camouflaged object detection to assess our methodology and compared it with state-of-the-art network models. On the largest dataset COD10K, our method achieved a Structure-measure of 0.827 and demonstrated superior performance in other evaluation metrics, outperforming recent network models.

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