LAVA: Leveraging Self-attention to Learn Video Features for Wastewater Pipe Anomaly Detection

Urban network data often originate from multiple sources and are represented in diverse formats, which can make their processing and analysis complex. In this work, we focus specifically on video data, particularly inspection television (ITV) videos of wastewater pipes. These videos play a crucial role in the management and maintenance of urban networks. On one hand, they help identify anomalies that may affect the pipes, such as obstructions or degradations. On the other hand, they provide essential information about the structural properties of the pipes and networks, including their diameter and the direction of wastewater flow. In this paper, we propose a classification algorithm for ITV videos, with a particular focus on detecting diameter changes, internal cracks, chemical attacks, and turbid-colored water within the pipes. This task is essential for predictive maintenance and hydraulic modeling of wastewater networks. We build on Video Vision Transformer (ViViT) and TimeSformer-based methodologies for video classification, which allow for the effective capture of both spatial and temporal relationships between the different frames in the video data. We specifically describe various mechanisms for generating training datasets from a subset of manually annotated images. The experimental study shows promising results on real-world ITV videos of wastewater pipe networks.