Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network

Abstract

With the support of big data and GPU acceleration training, the artificial intelligence technology with deep learning as its core is developing rapidly and has been widely used in many fields. At the same time, feature extraction operations are required by the current image-based corrosion damage detection method in the field of ships, with little effect but consuming the large amount of manpower and financial resources. Therefore, a new method for hull structural plate corrosion damage detection and recognition based on artificial intelligence using convolutional neural network is proposed. The convolutional neural network (CNN) model is trained through a large number of classified corrosion damage images to obtain a classifier model. Then the classifier model is used with overlap-scanning sliding window algorithm to recognize and position the location of corrosion damage. Finally, the damage detection pattern for hull structural plate corrosion damage as well as other types of superficial structural damage using convolutional neural network is proposed, which can accelerate the application of artificial intelligence technology into the field of naval architecture & ocean engineering.