AI Visual Inspection: Identify 30 Defects in Blow - Molded Products within 0.2 Seconds

The training dataset is then input into a deep - learning model, such as a Convolutional Neural Network (CNN). During the model training process, the network automatically extracts the feature information in the images by learning from a large number of image data, gradually mastering the unique visual characteristics of different defects. For example, the model can recognize that bubbles appear as circular highlight areas in the image, and scratches are manifested as slender grayscale - changing lines. As the training progresses, the model's ability to recognize defect features continues to improve, ultimately achieving accurate detection of defects in blow - molded products.

During actual production, high - speed industrial cameras collect images of blow - molded products in real - time and quickly transmit the image data to the AI visual inspection system. The system uses the trained deep - learning model to analyze and process the images, completing the identification and judgment of 30 types of defects within 0.2 seconds and outputting