The present invention is a computer implemented process related to a deep learning neural network architecture for generating an object detection model using high-resolution images. In particular, the present invention relates to improving the classification accuracy and reliability of edge inspection in contact lenses. The present invention is a computer implemented process for representing a software architecture, including software components and their interdependencies, representing core functional modules of an application. The system and method of the present invention capture a high-resolution image, transform the circular edge of the lens into a horizontal line representing the circular edge, limit the image size by removing pixel information around the edge, and parts that overlap the horizontal edge image , and vertically stacking the extracted images to form a single high-resolution image ideal for processing and analysis by a convolutional neural network after enhancing the original image with a new image generated by Generative Adversarial Networks (GAN). Enables accurate classification of defects.
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