基于补丁总边界变化的建筑物薄烟灵活感知方法

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-09-19 DOI:10.7717/peerj-cs.2282
Jieming Zhang, Yifan Gao, Xianchao Chen, Zhanchen Chen
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引用次数: 0

摘要

早期火灾预警对电力系统的安全性和稳定性至关重要。然而,目前的方法在捕捉细微特征方面遇到了挑战,从而限制了其在针对潜在火灾危险提供及时警报方面的有效性。为了克服这一缺陷,我们提出了一种新型薄烟检测算法,以增强早期火灾检测能力。其核心是首先提出了 "斑块-TBV "特征,并在斑块级别计算总边界变化(TBV)。这种方法源于这样一种认识,即传统方法难以检测到图像特征的微小变化,尤其是在特征分散或微妙的情况下。通过在更局部的水平上计算 TBV,所提出的算法在评估图像质量时获得了更精细的粒度,使其能够捕捉到可能表明存在烟雾或火灾早期迹象的细微变化。使我们的算法与众不同的另一个关键方面是加入了细微变化放大技术。这项技术利用计算出的 TBV 值放大图像中的细微特征。这种放大策略对于提高算法检测细微变化的精度至关重要,尤其是在烟雾浓度可能很小或很分散的环境中。为了评估该算法在实际场景中的性能,我们构建了一个名为 TIP 的综合数据集,其中包含 3,120 张图像。该数据集涵盖了实际应用中可能遇到的各种情况和潜在挑战。实验结果证实了所提算法的鲁棒性和有效性,展示了其在各种情况下提供准确、及时的火灾预警的能力。总之,我们的研究不仅发现了现有方法在捕捉细微特征进行早期火灾探测方面的局限性,还提出了一种复杂的算法,将 Patch-TBV 和微变异放大集成在一起,以应对这些挑战。该算法的有效性和稳健性通过广泛的测试得到了证实,证明了其作为加强电力系统和类似环境中消防安全的重要工具的潜力。
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A flexible perception method of thin smoke based on patch total bounded variation for buildings
Early fire warning is critical to the safety and stability of power systems. However, current methods encounter challenges in capturing subtle features, limiting their effectiveness in providing timely alerts for potential fire hazards. To overcome this drawback, a novel detection algorithm for thin smoke was proposed to enhance early fire detection capabilities. The core is that the Patch-TBV feature was proposed first, and the total bounded variation (TBV) was computed at the patch level. This approach is rooted in the understanding that traditional methods struggle to detect minute variations in image characteristics, particularly in scenarios where the features are dispersed or subtle. By computing TBV at a more localized level, the algorithm proposed gains a finer granularity in assessing image quality, enabling it to capture subtle variations that might indicate the presence of smoke or early signs of a fire. Another key aspect that sets our algorithm apart is the incorporation of subtle variation magnification. This technique serves to magnify subtle features within the image, leveraging the computed TBV values. This magnification strategy is pivotal for improving the algorithm’s precision in detecting subtle variations, especially in environments where smoke concentrations may be minimal or dispersed. To evaluate the algorithm’s performance in real-world scenarios, a comprehensive dataset, named TIP, comprising 3,120 images was constructed. The dataset covers diverse conditions and potential challenges that might be encountered in practical applications. Experimental results confirm the robustness and effectiveness of the proposed algorithm, showcasing its ability to provide accurate and timely fire warnings in various contexts. In conclusion, our research not only identifies the limitations of existing methods in capturing subtle features for early fire detection but also proposes a sophisticated algorithm, integrating Patch-TBV and micro-variation amplification, to address these challenges. The algorithm’s effectiveness and robustness are substantiated through extensive testing, demonstrating its potential as a valuable tool for enhancing fire safety in power systems and similar environments.
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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