Semantic deep learning and rule optimization for surface corrosion detection and evaluation

A method for training and/or using a semantic deep learning model to detect surface corrosion, comprising:

• training, by an application executing on one or more computing devices, the semantic deep learning model to detect and segment corrosion in images of an input dataset;
• applying an input dataset that includes a plurality of images of infrastructure to the trained semantic deep learning model to produce a semantically segmented output dataset, the semantically segmented output dataset including labeled corrosion segments;
• calculating a degree of corrosion index for each corrosion segment based on a mean grayscale value of pixels within the corrosion segment's area;
• and
• displaying, by the application, in a user interface or storing to memory/storage of the one or more computing devices, the degree of corrosion index of each corrosion segment.

A non-transitory electronic device readable medium having instructions stored thereon that when executed on one or more processors of one or more electronic devices are operable to:

• receive a training dataset that includes a plurality of images of infrastructure having at least some surface corrosion;
• apply an image segmentation algorithm to segment a first portion of the images of the training dataset;
• receive user-provided labels for at least some of the segments of the first portion to produce labeled images;
• optimize classification rules based on the labeled images of the first portion to produce a rule-based classifier;
• apply the rule-based classifier to a second portion of the images of the training dataset to automatically segment and label at least some of the segments of the second portion;
• and
• use the labeled training dataset to train a semantic deep learning model to detect and segment corrosion in images of an input dataset.