基于非平衡数据库的森林火灾图像检测的深度残差多分辨率特征和优化核ELM

IF 2.4 3区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY
Roohum Jegan, Gajanan K. Birajdar, Sangita Chaudhari
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引用次数: 0

摘要

随着气候模式的变化,野火的发生率不断增加,对人类生命和环境构成威胁,对农业和森林生态系统造成灾难性影响。因此,及时探测野火对于实施有效的减灾战略至关重要。本文提出了一种利用ResNet-18多分辨率特征和核极限学习机(KELM)来解决类不平衡问题的森林火灾图像检测新技术。提取和融合浅层和深层ResNet-18特征,创建一个综合特征集,代表森林火灾图像数据的局部和全局特征。多分辨率特征融合可以有效捕获较低层次的视觉模式和输入图像的复杂抽象表示。随后将融合的特征集输入到核极限学习机中,该机器可以有效地处理非线性数据模式,用于火灾探测等二元分类任务。然而,KELM的性能很大程度上依赖于它的超参数,这些超参数是使用基于newton - raphson的优化器(NRBO)算法进行优化的。超参数微调过程确保KELM在最佳设置下运行,最终提高火灾探测过程的准确性和可靠性。使用两个公开的数据库Forest Fire和Flame对该算法进行了评估,检测准确率分别为97.88%和99.88%。此外,每个特征对模型预测的贡献是使用SHAP (SHapley加性解释)来解释决策的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Residual Multi-resolution Features and Optimized Kernel ELM for Forest Fire Image Detection Using Imbalanced Database

The growing incidence of wildfires, intensified by changing climate patterns, poses risks to human lives and the environment, leading to catastrophic impacts on agricultural and forest ecosystems. Consequently, timely wildfire detection becomes imperative to implement effective mitigation strategies. This article presents a new forest fire image detection technique to address a class imbalance problem using ResNet-18 multi-resolution features and kernel extreme learning machine (KELM). Shallow and deep layer ResNet-18 features are extracted and fused to create a comprehensive feature set that represents local and global characterization of the forest fire image data. The multi-resolution feature fusion effectively captures lower-level visual patterns and complex and abstract representations of the input image. The fused feature set is subsequently input into a kernel extreme learning machine, which effectively handles nonlinear data patterns for binary classification tasks like fire detection. However, the performance of the KELM heavily relies on its hyperparameters, which are optimized using the Newton–Raphson-Based Optimizer (NRBO) algorithm. The hyperparameters fine-tuning process ensures that the KELM operates with optimal settings, ultimately enhancing the accuracy and reliability of the fire detection process. The proposed algorithm is evaluated using two publicly available databases, Forest Fire and Flame, with a detection accuracy of 97.88% and 99.88%, respectively. Moreover, the contribution of each feature to the model’s predictions to interpret decisions is elaborated using SHAP (SHapley Additive exPlanations).

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来源期刊
Fire Technology
Fire Technology 工程技术-材料科学:综合
CiteScore
6.60
自引率
14.70%
发文量
137
审稿时长
7.5 months
期刊介绍: Fire Technology publishes original contributions, both theoretical and empirical, that contribute to the solution of problems in fire safety science and engineering. It is the leading journal in the field, publishing applied research dealing with the full range of actual and potential fire hazards facing humans and the environment. It covers the entire domain of fire safety science and engineering problems relevant in industrial, operational, cultural, and environmental applications, including modeling, testing, detection, suppression, human behavior, wildfires, structures, and risk analysis. The aim of Fire Technology is to push forward the frontiers of knowledge and technology by encouraging interdisciplinary communication of significant technical developments in fire protection and subjects of scientific interest to the fire protection community at large. It is published in conjunction with the National Fire Protection Association (NFPA) and the Society of Fire Protection Engineers (SFPE). The mission of NFPA is to help save lives and reduce loss with information, knowledge, and passion. The mission of SFPE is advancing the science and practice of fire protection engineering internationally.
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