VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface
This work addresses road safety monitoring by detecting anomalies such as fallen trees or rocks, but it appears incremental as it combines existing auto-encoding and isolation-based methods.
The paper tackled the problem of detecting fallen objects on road surfaces using a hybrid deep learning approach, achieving anomaly detection on test images with added objects like stones and plywood and under snowy conditions.
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.