论文标题
vae-iforest：自动编码重建和基于隔离的异常，检测到道路表面上的物体
VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface
论文作者
论文摘要
在道路监测中，在早期阶段检测道路表面的变化以防止损害第三方是一个重要的问题。由于洪水或地震的外力，掉落的物体的目标可能是倒下的树，并且从斜坡上掉下了岩石。可以灵活地检测道路表面上下落物体的异常情况。我们原型原型将自动编码重建和基于隔离的异常检测器结合起来，以应用道路表面监测。实际上，我们将方法应用于一组测试图像，这些测试图像位于倒下的石头和胶合板上添加的原始输入中，并且在冬季道路上积雪。最后，我们提到了用于实际目的应用的未来工作。
In road monitoring, it is an important issue to detect changes in the road surface at an early stage to prevent damage to third parties. The target of the falling object may be a fallen tree due to the external force of a flood or an earthquake, and falling rocks from a slope. Generative deep learning is possible to flexibly detect anomalies of the falling objects on the road surface. We prototype a method that combines auto-encoding reconstruction and isolation-based anomaly detector in application for road surface monitoring. Actually, we apply our method to a set of test images that fallen objects is located on the raw inputs added with fallen stone and plywood, and that snow is covered on the winter road. Finally we mention the future works for practical purpose application.
