论文标题
基于兴趣的噪声衰减方法改善异常检测性能
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance
论文作者
论文摘要
准确提取驾驶事件是在基于轮胎摩擦鼻异常检测任务中最大化计算效率和异常检测性能的方法。这项研究提出了一种简洁且非常有用的方法，用于提高事件提取的精度，而事件提取的精度受到额外的噪声（例如风噪声）的阻碍，由于其随机性，这很难清楚地表征。该方法的核心是基于与感兴趣频率相对应的道路摩擦声音的识别，并使用多个频过滤器删除了相反的特性。我们的方法可以使驾驶事件提取的精确最大化，同时平均提高异常检测性能8.506％。因此，我们得出结论，我们的方法是一种适用于室外边缘计算环境中道路表面异常检测目的的实用解决方案。
Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. This study proposes a concise and highly useful method for improving the precision of the event extraction that is hindered by extra noise such as wind noise, which is difficult to characterize clearly due to its randomness. The core of the proposed method is based on the identification of the road friction sound corresponding to the frequency of interest and removing the opposite characteristics with several frequency filters. Our method enables precision maximization of driving event extraction while improving anomaly detection performance by an average of 8.506%. Therefore, we conclude our method is a practical solution suitable for road surface anomaly detection purposes in outdoor edge computing environments.
