Abstract: A visual inspection method, computing system and computing device are provided. An exemplary method includes providing a uniform group of manufactured products, performing a first process for detecting noisy label data within a training data set, acquiring a noisy data set and a clean data set according to the noisy label data detected based on the first process, performing a second process for training a vision inspection model based on the acquired noisy data set and clean data set, visually inspecting the products using the vision inspection model to identify defective products within the group, and removing the defective products from the group. The two stage vision inspection model training method and system prevents performance degradation due to mislabeled training data, maintains maximum recall on defective data, and improves precision for good product data.

Abstract: An image processing apparatus including a communication unit configured to receive at least one of training non-defect data and at least one of training defect data from an external device or a server, and a processor. The processor is configured to cause image degradation with respect to the training defect data according to a predetermined pattern or an arbitrary pattern, train an artificial intelligence generative model by using the degraded training defect data, extract defect information from a defect indicated by an image of a product, and generate final virtual defect data by inputting a second virtual defect data to the trained artificial intelligence generative model. In addition, the second virtual defect data is generated by synthesizing a first virtual defect data, a non-defect data and a first virtual mask image, and the first virtual defect data is generated based on the defect information.

Abstract: A method for monitoring judgment records for an outlier judgment guide according to an embodiment of the inventive concepts is configured to detect at least one similarity image having a similarity greater than or equal to a predetermined reference with a predetermined inspection image, provide judgment detailed information, which is information specifying a judgement content on the presence or absence of an outlier, acquire first judgment result information, which is information specifying a judgement result on the presence or absence of an outlier for the inspection image, and then generates the inspection image and a first judgment result depending on a similarity between each of the inspection image and the similarity image.

Abstract: A product inspection apparatus including a camera configured to capture an image of a product to be inspected; and a processor configured to extract defect information from a defect indicated by the captured image of the product, generate first virtual defect data including at least one of a location, a size and a shape of the defect included in the captured image, based on the extracted defect information, generate second virtual defect data by synthesizing the first virtual defect data with non-defect data representing the product without the defect, and generate final virtual defect data by inputting the second virtual defect data to an artificial intelligence generative model.

Abstract: A computing device for performing anomaly detection according to the present disclosure includes a memory including at least one memory bank that is a logical area, and a processor which: is configured to train a neural network by using at least one function module; generating a reduced feature extraction module; generating the memory bank on the basis of reduced feature data generated from the reduced feature extraction module; and determining whether inspection data is normal by using the generated reduced feature extraction module and the memory bank.

Abstract: A product inspection apparatus including a camera configured to capture an image of a product to be inspected; and a processor configured to extract defect information from a defect indicated by the captured image of the product, generate first virtual defect data including at least one of a location, a size and a shape of the defect included in the captured image, based on the extracted defect information, generate second virtual defect data by synthesizing the first virtual defect data with non-defect data representing the product without the defect, and generate final virtual defect data by inputting the second virtual defect data to an artificial intelligence generative model.

Abstract: A plurality of images captured using a camera included in a robotic system are analyzed. A spatial map is generated using a sensor included in the robotic system. A semantic location map is generated using at least the analyzed plurality of captured images and the generated spatial map. A natural language input referencing a desired product item is received from a user. A speech recognition result is recognized from the natural language input and sent to a reasoning engine. In response to sending the recognized speech recognition result, one or more commands for the robotic system are received from the reasoning engine. The received one or more commands are performed and feedback to the user based on at least one of the one or more commands is provided.

Abstract: A system comprises a motor and a base. The motor is configured to control the physical movement of the system and the base is configured to be coupled with a computing device. The computing device is configured to receive an indication of a target object and detect an obstacle object in a physical environment of the system. In response to detecting an obstacle object, the computing device provides to a user an inquiry associated with the detected obstacle object and receives a natural language response. Based at least in part on the natural language response, the computing device controls the motor to navigate the system around the detected obstacle object. Once the target object is detected, the computing device provides an indication that the target object has been located.

Abstract: A plurality of images captured using a camera included in a robotic system are analyzed. A spatial map is generated using a sensor included in the robotic system. A semantic location map is generated using at least the analyzed plurality of captured images and the generated spatial map. A natural language input referencing a desired product item is received from a user. A speech recognition result is recognized from the natural language input and sent to a reasoning engine. In response to sending the recognized speech recognition result, one or more commands for the robotic system are received from the reasoning engine. The received one or more commands are performed and feedback to the user based on at least one of the one or more commands is provided.