Abstract: Methods, systems, and computer-readable storage media for receiving a product image depicting a product that is to-be-inspected for defects, transmitting a request to a LLM system, the request including the product image and a reference image, receiving, from the LLM system, a textual response, the textual response being generated by the LLM system at least partially by processing the product image and the reference image, processing the textual response and the product image through a RIS model to provide an output image that includes one or more masks, each mask indicating a defect in a product depicted in the product image, and providing the output image with an indication that the product is defective.

Abstract: Methods, systems, and computer-readable storage media for processing a target image and a reference image through a segmentation model to provide a set of target masks and a set of reference masks, and determining that a difference exists between a target mask and a reference mask, and in response, providing a potential defect patch for a ROI of the target image corresponding to the difference, generating a potential defect embedding using the potential defect patch, comparing the potential defect embedding to each defect embedding in a set of defect embeddings to provide a set of similarity scores, and selectively indicating presence of a defect in the product based on a similarity score in the set of similarity scores.

Abstract: Systems and methods described herein relate to semantic feature extraction for auto-labeling of defects. An image is processed to obtain structured text data describing a target defect of an item appearing in the image. The structured text data is processed to convert the structured text data into a target embedding associated with the target defect. The target embedding is automatically compared with a plurality of reference embeddings to obtain a comparison result. The plurality of reference embeddings is stored in a reference data structure that associates each reference embedding with a respective defect label. Based on the comparison result, the reference data structure is updated and assignment of a target defect label to the image is initiated. Such operations may be performed for each of a plurality of images in an unlabeled dataset.

Abstract: Methods, systems, and computer-readable storage media for a defect detection system that generates synthetic defect data that is representative of real-world defects in products. The synthetic defect data is included in training data for training a defect detection model. The defect detection model is deployed to a production environment to detect defects in products.

Abstract: Systems and methods described herein relate to semantic feature extraction for auto-labeling of defects. An image is processed to obtain structured text data describing a target defect of an item appearing in the image. The structured text data is processed to convert the structured text data into a target embedding associated with the target defect. The target embedding is automatically compared with a plurality of reference embeddings to obtain a comparison result. The plurality of reference embeddings is stored in a reference data structure that associates each reference embedding with a respective defect label. Based on the comparison result, the reference data structure is updated and assignment of a target defect label to the image is initiated. Such operations may be performed for each of a plurality of images in an unlabeled dataset.

Abstract: Systems and methods described herein relate to multi-modal defect inspection. An image of an inspected item and structural imaging data of the inspected item are obtained. The image is captured by at least one optical sensor and the structural imaging data is obtained via at least one structural imaging sensor. The image is processed via a first machine learning model to obtain a first defect classification. At least some of the structural imaging data is processed via a second machine learning model to obtain a second defect classification. An inspection result is automatically generated based on the first defect classification and the second defect classification. The inspection result is caused to be presented at a user device in association with an item identifier of the inspected item.

Abstract: A pipeline-integrated measuring device includes a thrust fixed frame, a measuring fixed frame, a measuring moving frame and strain spokes. The measuring fixed frame is fixed on the thrust fixed frame and is connected to the measuring moving frame by means of the strain spokes. A first propellant supply connecting nozzle is arranged on the measuring fixed frame and is used to be in communication with a propellant supply pipeline on a test bench; propellant supply channels are arranged in the strain spokes; a second propellant supply connecting nozzle is arranged on the measuring moving frame and is used to be connected to an engine inlet pipeline; first sensors, second sensors and third sensors are arranged on the strain spokes; and the first sensors, the second sensors and the third sensors are arranged at maximum strain force positions in axial directions and lateral directions of the strain spokes respectively.

Abstract: Methods, systems, and computer-readable storage media for receiving a product image depicting a product that is to-be-inspected for defects, transmitting a request to a LLM system, the request including the product image and a reference image, receiving, from the LLM system, a textual response, the textual response being generated by the LLM system at least partially by processing the product image and the reference image, processing the textual response and the product image through a RIS model to provide an output image that includes one or more masks, each mask indicating a defect in a product depicted in the product image, and providing the output image with an indication that the product is defective.

Abstract: Methods, systems, and computer-readable storage media for a defect detection system that generates synthetic defect data that is representative of real-world defects in products. The synthetic defect data is included in training data for training a defect detection model. The defect detection model is deployed to a production environment to detect defects in products.

Abstract: A method includes determining a plurality of data points, determining a distance between each data point and each of the other plurality of data points, choosing a first one of the plurality of data points, identifying all of the other plurality of data points within a maximum distance of the chosen data point, repeating steps the previous steps to choose a different one of the plurality of data points until all of the data points have been chosen, identifying one or more clusters each having a predefined minimum number, K, of data points within a predefined search radius and analyzing the one or more clusters with respect to K linkages.

Abstract: A method includes determining a plurality of data points, determining a distance between each data point and each of the other plurality of data points, choosing a first one of the plurality of data points, identifying all of the other plurality of data points within a maximum distance of the chosen data point, repeating steps the previous steps to choose a different one of the plurality of data points until all of the data points have been chosen, identifying one or more clusters each having a predefined minimum number, K, of data points within a predefined search radius and analyzing the one or more clusters with respect to K linkages.