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.
翻译:精确地提取驾驶事件是最大限度地提高轮胎摩擦鼻鼻异常探测任务计算效率和异常现象检测性能的方法。本研究报告提出了一个简明和非常有用的方法,用于改进因风声等额外噪音而阻碍的事件提取的精确性,这种噪音因其随机性而难以明确定性。拟议方法的核心在于确定与兴趣频率相对应的公路摩擦声音,并用几个频率过滤器去除相反的特征。我们的方法可以精确地尽可能扩大驾驶事件提取量,同时平均提高8.506%的异常现象检测性能。因此,我们得出的结论是,我们的方法是一种实用的解决办法,适合于在户外边缘计算环境中探测公路表面异常现象。