Boundary-Guided Camouflaged Object Detection
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866918127552430080 |
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| author | Sun, Yujia Wang, Shuo Chen, Chenglizhao Xiang, Tian-Zhu |
| author_facet | Sun, Yujia Wang, Shuo Chen, Chenglizhao Xiang, Tian-Zhu |
| contents | Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_00794 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Boundary-Guided Camouflaged Object Detection Sun, Yujia Wang, Shuo Chen, Chenglizhao Xiang, Tian-Zhu Computer Vision and Pattern Recognition Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet. |
| title | Boundary-Guided Camouflaged Object Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2207.00794 |