Advancing Industrial inspection: A Dataset for Automated Damage Detection in Insulated Pipes
The application of automated inspection for industrial pipe damage detection is attracting substantial research and development interest. Damage to pipes not only hinders the optimal functioning of factories but also presents a risk of industrial disasters, making the adoption of automated solutions imperative. The use of Unmanned Aerial Vehicles (UAVs) equipped with Deep Neural Network (DNN)-enhanced vision offers an innovative method to detect pipe damage in real-time or during video post-processing. However, a major challenge in fully leveraging the capabilities of DNNs for pipe damage detection is the lack of specialized, well-structured, and annotated public datasets for DNN training.
This paper introduces the Pipes Damages Image (PDI) dataset, the first of its kind, specifically designed for detecting damage in insulated industrial pipes. This carefully compiled dataset covers a wide range of industrial settings, with each scenario meticulously annotated for damage detection. It also provides base-line results from state-of-the-art visual object detection models.