Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance

• URL: http://arxiv.org/abs/2605.25385v1
• Date: Mon, 25 May 2026 03:26:13 GMT
• Title: Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance
• Abstract summary: We introduce a new weakly supervised approach for camouflaged object detection to overcome limitations.<n>Specifically, we propose a novel network, MGNet, which tackles edge ambiguity and missed detections.<n>For the weak supervision challenge, we propose BoxSAM, which leverages the Segment Anything Model (SAM) with bounding-box prompts to generate pseudo-labels.
• Score: 49.06684374007753
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existing fully supervised methods require labor-intensive pixel-level annotations, making weakly supervised methods a viable compromise that balances accuracy and annotation efficiency. However, weakly supervised methods often experience performance degradation due to the use of coarse annotations. In this paper, we introduce a new weakly supervised approach for camouflaged object detection to overcome these limitations. Specifically, we propose a novel network, MGNet, which tackles edge ambiguity and missed detections by utilizing initial masks generated by our custom-designed Cascaded Mask Decoder (CMD) to guide the segmentation process and enhance edge predictions. We introduce a Context Enhancement Module(CEM) to reduce the missing detection, and a Mask-guided Feature Aggregation Module (MFAM) for effective feature aggregation. For the weak supervision challenge, we propose BoxSAM, which leverages the Segment Anything Model (SAM) with bounding-box prompts to generate pseudo-labels. By employing a redundant processing strategy, high quality pixel-level pseudo-labels are provided for training MGNet. Extensive experiments demonstrate that our method delivers competitive performance against current state-of-the-art methods.

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