标题： 基于变分自编码器和一类SVM的人行道危险检测 显示英文标题

标题： Sidewalk Hazard Detection Using Variational Autoencoder and One-Class SVM

摘要： 户外环境的不可预测性带来了许多安全问题，使得危险检测对于安全导航至关重要。 本文介绍了一种新颖的系统，利用变分自编码器（VAE）与单类支持向量机（OCSVM）相结合的混合方法，实现人行道安全导航。 该系统旨在检测可能构成行走危险的人行道异常情况。 使用视频记录收集了一个包含超过15,000个训练帧和5,000个测试帧的数据集，捕捉了各种人行道场景，包括正常和危险状况。 在部署过程中，VAE利用其重建机制检测帧内的异常。 VAE的重建效果差意味着存在异常，之后使用OCSVM确认该异常是危险的还是非危险的。 所提出的VAE模型表现出色，AUC值高达0.94，能够有效区分可能成为危险的异常。 OCSVM用于减少对虚假危险异常的检测，例如井盖或水阀盖。 这种方法的准确率达到91.4%，为区分危险和非危险场景提供了高度可靠的系统。 这些结果表明，所提出的系统为不确定环境中的危险检测提供了一个稳健的解决方案。 显示英文摘要

摘要： The unpredictable nature of outdoor settings introduces numerous safety concerns, making hazard detection crucial for safe navigation. This paper introduces a novel system for sidewalk safety navigation utilizing a hybrid approach that combines a Variational Autoencoder (VAE) with a One-Class Support Vector Machine (OCSVM). The system is designed to detect anomalies on sidewalks that could potentially pose walking hazards. A dataset comprising over 15,000 training frames and 5,000 testing frames was collected using video recordings, capturing various sidewalk scenarios, including normal and hazardous conditions. During deployment, the VAE utilizes its reconstruction mechanism to detect anomalies within a frame. Poor reconstruction by the VAE implies the presence of an anomaly, after which the OCSVM is used to confirm whether the anomaly is hazardous or non-hazardous. The proposed VAE model demonstrated strong performance, with a high Area Under the Curve (AUC) of 0.94, effectively distinguishing anomalies that could be potential hazards. The OCSVM is employed to reduce the detection of false hazard anomalies, such as manhole or water valve covers. This approach achieves an accuracy of 91.4%, providing a highly reliable system for distinguishing between hazardous and non-hazardous scenarios. These results suggest that the proposed system offers a robust solution for hazard detection in uncertain environments.