Abstract: A deep learning-based quality inspection system by learning only non-defective manufactured product data may include: an input unit receiving a non-defective manufactured product image data set; a preprocessor preprocessing a model to learn the images with a same size by not applying a cropping task to cut and process only an area at a specific location within each image for each of the plurality of images included in the image data set, but applying a resizing task of adjusting each image to a desired size and a padding task of adjusting the size of the image while maintaining a ratio of each image as it is; and a controller extracting a non-defective manufactured product feature which becomes a non-defective manufactured product criterion from the preprocessed image, and generating a plurality of fake defective manufactured product features by adding a Gaussian noise feature to the extracted non-defective manufactured product feature.

Abstract: Embodiments relate to a deep learning/AI-based product surface quality inspection system which is accurate and reliable in product quality inspection which is a core task in an injection process among various manufacturing fields. The system can provide, to a user, better performance than non-defective/defective manufactured product classification methodology which is an existing commonly used method through a method and a system considering characteristics of a factory environment and an actual product production process for all pipelines of product quality inspection by using only a non-defective manufactured product image unlike most injection process surface inspection AIs developed to date.

Abstract: Disclosed are a product surface inspecting apparatus and method which detect a defect on products having different feature using a previously trained artificial neural network. A product surface inspecting apparatus according to an exemplary embodiment includes a sensor unit which photographs a product to generate image data and measures at least one of a color, a saturation, a brightness, a transparency, and a reflectance of the product; and a detection unit which detects a defect on a product by inputting the image data to a convolutional neural network trained to detect a defect on a product surface, the number of convolution layers of the convolutional neural network may be determined based on at least one of the color, the saturation, the brightness, the transparency, and the reflectance of the product.

Abstract: A thermal image-based hybrid object detection method using a YOLO model and an object tracking technique includes: a) a step of obtaining a plurality of thermal image data by means of a thermal image camera; b) a step of extracting an object region from a thermal image data of the thermal image camera using an installed YOLO model and then creating an object region coordinate data by returning coordinates of the object region by means of an image processor; and c) a step of outputting a first image reflecting an object region coordinate data to each of thermal image data received from the image processor by means of an output device when the output device requests transmission of the first image.