基于对比学习和伪异常合成的无监督火灾检测  

Unsupervised Fire Detection Based on Contrastive Learning and Synthetic Pseudo Anomalies

作　　者：叶伟华 吴云涛 李佐勇 YE Wei-Hua;WU Yun-Tao;LI Zuo-Yong(Fuzhou Software Park Technology Innovation Development Co.Ltd.,Fuzhou 350101,China;College of Computer Science and Mathematics,Fujian University of Technology,Fuzhou 350118,China;College of Computer and Control Engineering,Minjiang University,Fuzhou 350121,China;Fujian Provincial Key Laboratory of Information Processing and Intelligent Control,Fuzhou 350121,China)

机构地区：[1]福州软件园科技创新发展有限公司,福州350101 [2]福建理工大学计算机科学与数学学院,福州350118 [3]闽江学院计算机与控制工程学院,福州350121 [4]福建省信息处理与智能控制重点实验室,福州350121

出　　处：《计算机系统应用》2024年第6期28-36,共9页Computer Systems & Applications

基　　金：国家自然科学基金(61972187)。

摘　　要：传统的火灾检测方法大多基于目标检测技术,存在火灾样本获取难度高、人工标注成本高的问题.为解决该问题,本研究提出了一种基于对比学习和伪异常合成的无监督火灾检测模型.为了实现无监督图像特征学习,提出了交叉输入对比学习模块.然后,引入了一个记忆原型学习正常场景图像的特征分布,通过特征重建实现对火灾场景的判别.并且,提出了伪异常火灾场景合成方法和基于欧氏距离的异常特征区分损失,使模型对于火灾场景具有针对性.根据实验表明,我们的方法在Fire-Flame-Dataset和Fire-Detection-Image-Dataset两个公开火灾检测数据集上的图像级AUC分别达到89.86%和89.56%,优于PatchCore、PANDA、Mean-Shift等主流图像异常检测算法.Traditional fire detection methods are mostly based on object detection techniques,which suffer from difficulties in acquiring fire samples and high manual annotation costs.To address this issue,this study proposes an unsupervised fire detection model based on contrastive learning and synthetic pseudo anomalies.A cross-input contrastive learning module is proposed for achieving unsupervised image feature learning.Then,a memory prototype that learns the feature distribution of normal scene images to discriminate fire scenes through feature reconstruction is introduced.Moreover,a method for synthesizing pseudo anomaly fire scenes and an anomaly feature discrimination loss based on Euclidean distance are proposed,making the model more targeted toward fire scenes.Experimental results demonstrate that the proposed method achieves an image-level AUC of 89.86% and 89.56% on the publicly available Fire-FlameDataset and Fire-Detection-Image-Dataset,respectively,surpassing mainstream image anomaly detection algorithms such as PatchCore,PANDA,and Mean-Shift.

关 键 词：火灾检测 异常检测 对比学习 记忆机制 无监督学习 

分 类 号：X932[环境科学与工程—安全科学] TP391.41[自动化与计算机技术—计算机应用技术]