Defect Detection in Metal Surfaces

Raw materials like plywood, metal, and plastics, often undergo rigorous quality control measures to ensure they meet the needs and requirements of the industries in which they’re used. In some cases, such materials might be classified into different grades based on the type and quantity of defects present, while in other cases materials may be discarded for having defects.

With the rise of Industrial IoT (IIoT) and Industry 4.0, ML is playing an ever-increasing role in enabling automation. This includes the use of computer vision to analyze and detect defects with products like raw materials on assembly lines, as well as out in the field (e.g., to detect metal fatigue).

With this in mind, we built an ML model that can classify defects on metal materials using image recognition. This involved preparing and wrangling the training data, building a .csv file to map that data to the classifications, and iterating with the model in PerceptiLabs.

Dataset Sample


Model Summary:

Component 1: ResNet50 include_top=No, input_shape=(224,224)

Component 2: Dense Activation=ReLU, Neurons=128

Component 3: Dense Activation=Softmax, Neurons=10

Statistics View

Accuracy Plot