论文标题
带有卷积神经网络的售后市场摩托车阻尼系统上的实时机油泄漏检测
Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks
论文作者
论文摘要
在这项工作中，我们详细描述了深度学习和计算机视觉如何帮助检测AirTender系统的故障事件，AirTender系统是售后摩托车阻尼系统组件。监视Airtender功能的最有效方法之一是在其表面寻找油污渍。从实时图像开始，首先在摩托车悬架系统中检测到Airtender，模拟室内，然后，二元分类器确定AirTender是否在溢出油。检测是在Yolo5架构的帮助下进行的，而分类是在适当设计的卷积神经网络OilNet40的帮助下进行的。为了更清楚地检测到油的泄漏，我们用荧光染料稀释机器中的油，激发波长峰值约为390 nm。然后用合适的紫外线LED照亮飞行员。整个系统是设计低成本检测设置的尝试。板载设备（例如迷你计算机）位于悬架系统附近，并连接到完整的高清摄像头框架架上。板载设备通过我们的神经网络算法，然后能够将Airtender定位并分类为正常功能（非泄漏图像）或异常（泄漏图像）。
In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component. One of the most effective ways to monitor the AirTender functioning is to look for oil stains on its surface. Starting from real-time images, AirTender is first detected in the motorbike suspension system, simulated indoor, and then, a binary classifier determines whether AirTender is spilling oil or not. The detection is made with the help of the Yolo5 architecture, whereas the classification is carried out with the help of a suitably designed Convolutional Neural Network, OilNet40. In order to detect oil leaks more clearly, we dilute the oil in AirTender with a fluorescent dye with an excitation wavelength peak of approximately 390 nm. AirTender is then illuminated with suitable UV LEDs. The whole system is an attempt to design a low-cost detection setup. An on-board device, such as a mini-computer, is placed near the suspension system and connected to a full hd camera framing AirTender. The on-board device, through our Neural Network algorithm, is then able to localize and classify AirTender as normally functioning (non-leak image) or anomaly (leak image).
