Padnet: a Patch-Based Anomaly Detection Framework for Industrial Pipeline Damage Detection

Abstract

Industrial pipeline inspection in petrochemical refineries is dangerous, expensive, time-consuming and prone to errors. Anomaly detection can play a crucial role towards its automation. Damages in this type of infrastructure are few and can be considered as anomalies (essentially outliers). This paper proposes a novel patch-based Anomaly Detection Network (PADNet), that employs deep learning for detecting insulated pipe damages. It consists of three main components: a) a pipeline segmentation module, b) an image patch proposal module, and c) an anomaly detection module. These components work sequentially first to localize insulated pipelines in the input UAV or ground camera images or video frames and then analyze image patches to detect and localize any damages. Importantly, the anomaly detection module can be trained using undamaged pipeline image data only, hence eliminating the need for costly damaged pipeline image annotation. Experimental results demonstrate the effectiveness of the proposed PADNet method in detecting pipeline damages, making it a promising solution for autonomous industrial infrastructure inspection.