Abstract: Systems, and methods, and devices for active learning are provided. The system includes a data storage device, dataset analysis tool, synthetic data generation module, anomaly module, image search engine module, explainable AI module, automated training platform, and optionally, a federated learning module. The system may be configured to operate on a general purpose or purpose-built computer, and may further include a processor, memory, and network interface. The system, through interaction of its constituent components, analyzes a provided dataset and generates synthetic data to augment data within the provided dataset. This provided data and generated data is used to train a machine learning model. The system may be operated iteratively to continuously improve the machine learning model trained by the system by applying explainable artificial intelligence techniques with little to no human intervention.

Abstract: Provided is a system and method for visual inspection. The system may be used in a quality assurance station at a manufacturing facility or site. The system may evaluate and determine the quality of manufactured or fabricated articles. The system may include a mechanical subsystem for capturing images of the article. The system may include a sensor such as a camera for capturing data, such as images. The system may include an artificial intelligence system to determine if the article suffers from an impermissible defect.

Abstract: A system and method for automated visual inspection is provided herein. The method includes providing an inspection image of the article to an object detection model trained to detect at least one defect type in an input image and generating object location data identifying a location of a detected object in the inspection image; comparing the inspection image to a golden sample image to identify an artifact in the inspection image corresponding to a difference between the inspection image and the golden sample image, wherein the artifact is defined by artifact location data describing a location of the artifact in the inspection image; and determining whether the artifact location data matches the object location data according to predetermined match criteria.

Abstract: Systems, methods, and computer devices for automated artificial intelligence visual inspection using a multi-model architecture are provided. The computer device includes a communication interface for receiving image data; a memory for storing the image data, a first neural network model, a second neural network model, and a second neural network model triggering condition; and a processor in communication with the memory. The processor is configured to: perform a first object detection task on the image data using the first neural network model; store first neural network model output data in the memory; determine whether the first neural network model output data satisfies the second model triggering condition; and, if the first neural network model output data satisfies the second model triggering condition: perform a second object detection task on the image data using the second neural network model.

Abstract: Provided is a system and method for visual inspection. The system may be used in a quality assurance station at a manufacturing facility or site. The system may evaluate and determine the quality of manufactured or fabricated articles. The system may include a mechanical subsystem for capturing images of the article. The system may include a sensor such as a camera for capturing data, such as images. The system may include an artificial intelligence system to determine if the article suffers from an impermissible defect.