AI-based apparatus and method for detecting a defect of a product

An AI-based apparatus for detecting a defect of a product, comprising:

• a sensor unit;
• a detection unit;
• a preprocessor configured to:
• photograph a product to generate image data and measure at least one of a color, a saturation, a brightness, a transparency, and a reflectance of the product;
• and
• detect a defect on the product by inputting the image data to a convolutional neural network (CNN) trained to detect a defect on a product surface;
• convert at least one of the color, the saturation, and the brightness of the image data with respect to at least one of the color, the saturation, and the brightness of the product used as training data of the convolutional neural network;
• wherein a number of convolution layers of the convolutional neural network is adjusted based on a defect detection difficulty determined according to at least one of the color, the saturation, the brightness, the transparency, and the reflectance of the product and a defect type, and
• wherein the convolutional neural network performs a learning by receiving a predetermined size of training image data for a first defect type to divide the training image data into grids having a predetermined size, wherein the training image data is configured by a three-channel image obtained by dividing an image for the same product with respect to RGB and a size of the training image data is 448×448, and a size of the grid is 7×7;
• and
• wherein the convolutional neural network performs the learning as many as the number obtained by dividing a size of one training image data by a size of the grid.