Deep learning for Coating Condition Assessment with Active perception

Protective coatings are the primary means of protecting marine and offshore structures from corrosion. Coating breakdown and corrosion (CBC) evaluation is the primary method of coating failure management. Evaluation methods can result in unnecessary maintenance costs and a higher risk of failure. To achieve a comprehensive collection of data for CBC assessment, an unmanned arial system (UAS), assisted by the latest technological innovations, will be used to facilitate data collection in inaccessible locations. An image-based CBC assessment system is developed to provide objective assessment of the severity of coating failure. This method is more suitable for inspecting large areas by capturing and analyzing pictures/videos of the target area than the surveyor's existing manual inspection solution. In this paper, deep learning-based object detection in the CBC assessment system has been developed to provide an effective CBC assessment for the marine and offshore industries. This will greatly improve the efficiency and reliability of coating inspection.