基于SAM模型和掩模制导的弱监督伪装目标检测

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xia Li, Xinran Liu, Lin Qi, Junyu Dong
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引用次数: 0

摘要

由于目标与其周围环境高度相似,从单幅图像中进行伪装目标检测是一项具有挑战性的任务。现有的完全监督方法需要耗费大量人力的像素级注释,这使得弱监督方法成为一种平衡准确性和注释效率的可行方法。然而,弱监督方法经常会因为使用粗糙的注释而导致性能下降。在本文中,我们引入了一种新的弱监督方法来克服这些限制。具体来说,我们提出了一种新的网络MGNet,它通过使用我们定制设计的级联掩码解码器(CMD)生成的初始掩码来指导分割过程并增强边缘预测,从而解决边缘模糊和遗漏检测问题。我们引入了上下文增强模块(CEM)来减少缺失检测,并引入了掩码引导的特征聚合模块(MFAM)来进行有效的特征聚合。对于弱监督挑战,我们提出了BoxSAM,它利用带有边界框提示的分段任意模型(SAM)来生成伪标签。采用冗余处理策略,为MGNet的训练提供了高质量的像素级伪标签。大量的实验表明,我们的方法与目前最先进的方法相比具有竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weakly supervised camouflaged object detection based on the SAM model and mask guidance
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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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
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