基于PVTv2的伪装目标检测边缘引导语义感知网络

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hongbo Bi, Jianing Yu, Disen Mo, Shiyuan Li, Cong Zhang
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引用次数: 0

摘要

伪装对象检测(COD)试图识别和分割视觉上融入周围环境的对象,这在复杂的现实世界场景中提出了重大挑战。尽管越来越多的人关注,现有的COD方法往往产生不满意的性能,主要是由于它们没有充分整合边缘信息和语义上下文,这是处理复杂场景时的一个关键缺点。为此,我们提出了一种新的边缘引导语义感知网络(ESNet),它明确地利用了边缘线索和多尺度语义之间的协同作用。我们的框架包含两个关键组件:一个是带有边缘指导的上下文感知聚合(CAEG)模块,它利用边缘信息来细化对象边界并增强跨尺度的特征表示;另一个是跨层语义细化融合(CSF)模块,旨在聚合和强化多层次语义上下文,以获得更丰富的特征表征。在三个具有挑战性的基准数据集上进行的大量实验表明,所提出的ESNet优于17种最先进的算法,在检测精度和鲁棒性方面达到了新的标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge-guided semantic-aware network for camouflaged object detection with PVTv2
Camouflaged object detection (COD) attempts to identify and segment objects visually blended into their surroundings, presenting significant challenges in complex real-world scenarios. Despite growing attention, existing COD methods often yield unsatisfactory performance, primarily due to their inadequate integration of edge information and semantic context—a critical shortcoming when handling intricate scenes. To this end, we propose a novel Edge-guided Semantic-aware Network (ESNet) that explicitly leverages the synergy between edge cues and multi-scale semantics. Our framework incorporates two key components: a Context-Aware Aggregation with Edge Guidance (CAEG) module, which utilizes edge information to refine object boundaries and enhance feature representation across scales, and a Cross-layer Semantic-Refinement Fusion (CSF) module, designed to aggregate and reinforce multi-level semantic context for richer feature characterization. Numerous experiments on three challenging benchmark datasets demonstrate that the proposed ESNet outperforms 17 state-of-the-art algorithms, achieving new standards in detection accuracy and robustness.
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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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