System and method for intelligently monitoring the production line that can monitor an inspected object image captured by an image capturing device, thereby allowing an operating station host to provide a labeling module to reinspect a classification decision of a classifier subsystem, to achieve the purpose of verifying the classification decision or checking whether there are missed inspections. In addition, the classifier subsystem can automatically filter out classification decisions with lower reliability to effectively reduce the number of reinspection. Moreover, a group of inspected object images can be analyzed first to obtain image difference features through comparison, which is suitable for insufficient training samples. Furthermore, the labeling module can simultaneously reinspect highly relevant historical classification decisions. Meanwhile, a second image capturing device is provided, so that the system can automatically label defect positions based on the inspected object image before and after repair, thereby learning to judge whether defects occur.

Systems, methods, and non-transitory computer readable media are provided for displaying and annotating map-based geolocation data at an augmented reality (AR) headset. Users with access to the map-based geolocation data can create or confirm annotations for geospatial data that may be sent to the server computer and transmitted back to the headset of the user as well as different AR headsets associated with other users.

A method for generating a neural network for detecting one or more objects in images includes generating one or more self-supervised proposal learning losses based on the one or more proposal features and corresponding proposal feature predictions. One or more consistency-based proposal learning losses are generated based on noisy proposal feature predictions and the corresponding proposal predictions without noise. A combined loss is generated using the one or more self-supervised proposal learning losses and one or more consistency-based proposal learning losses. The neural network is updated based on the combined loss.

Provided are a learning data collection apparatus, a learning data collection method, and a program for collecting learning data to be used for efficient retraining. A learning data collection apparatus (10) includes an inspection image acquisition unit (11) that acquires an inspection image, a region detection result acquisition unit (damage detection result acquisition unit (13)) that acquires a region detection result the region detection result indicating a region detected by a region detector that is trained, a correction history acquisition unit (15) that acquires a correction history of the region detection result, a calculation unit (17) that calculates correction quantification information obtained by quantifying the correction history, a database that stores the inspection image, the region detection result, and the correction history in association with each other, an image extraction condition setting unit (19) that sets a threshold value of the correction quantification information as an extraction condition, the extraction condition being a condition for extracting the inspection image to be used for retraining from the database, and a first learning data extraction unit (21) that extracts, as learning data for retraining the region detector, the inspection image satisfying the extraction condition and the region detection result and the correction history that are associated with the inspection image.

In one example, a method performed by a processing system in a telecommunications network includes acquiring live footage of an event, acquiring sensor data related to the event, wherein the sensor data is collected by a sensor positioned in a location at which the event occurs, extracting an analytical statistic related to a target participating in the event, wherein the extracting is based on content analysis of the live footage and the sensor data, filtering data relating to the target based on the analytical statistic to identify content of interest in the data, wherein the data comprises the live footage, the sensor data, and data relating to historical events that are similar to the event, and generating computer-generated content to present the content of interest, wherein when the computer-generated content is synchronized with the live footage on an immersive display, an augmented reality media is produced.

Systems, methods, and computer program products leverage artificial intelligence, and machine learning to process image enhancements using image enhancement techniques and algorithms. Image enhancements are determined to be best suited for enhancing each image as a function of each images' calculated validation parameters by an analytics engine. The images are each categorized by the image quality as a function of the validation parameters. Images identified as having an improvement space are further processed by querying the images' validation parameters using a knowledge base comprising historical data describing past image enhancements and historical validation parameters to the current image. A matrix of recommended enhancements, along with a predicted success rate for improving the image quality is provided to a user interface. A user can select one or more enhancements to apply to the image(s) and further provide feedback to the knowledge base, further improving enhancement recommendations and success rates.

A method for detecting anomalies in a system. The method includes collecting training data from the system, converting the training data into training images using an image generator, and designating each of the training images as corresponding to events for the system, where the events are at least one of an expected normal event and a non-normal event. The method further includes generating an image recognition model based on the training images and the designations thereof. The method further includes collecting new data from the system, converting the new data into input images, and analyzing the input images using the image recognition model to determine which of the events for the system are represented in the input images, where the anomalies are detected when the input images are determined to at least one of represent a non-normal event and fail to represent an expected normal event.

An apparatus, methods and systems for collecting data related to an industrial environment are disclosed. A monitoring system can include a data collector communicatively coupled to a plurality of input channels relating to an aspect of an industrial production process, a data storage structured to store a plurality of detection values, a data analysis circuit structured to interpret a subset of the detection values to determine a state value comprising at least one of a process state or a component state, an optimization circuit structured to analyze a subset of the detection values and the state value, using at least one of a neural net or an expert system, to provide an adjustment recommendation, and an analysis response circuit structured to adjust the industrial production process in response to the adjustment recommendation.

An analysis device includes an analysis unit configured to receive scattered light, transmitted light, fluorescence, or electromagnetic waves from an observed object located in a light irradiation region light-irradiated from a light source and analyze the observed object on the basis of a signal extracted on the basis of a time axis of an electrical signal output from a light-receiving unit configured to convert the received light or electromagnetic waves into the electrical signal.

A learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, acquires a plurality of discrimination results obtained by causing a plurality of learned discriminators to discriminate the same input image, determines the correct answer data of the input image on the basis of the plurality of discrimination results, causes the discriminator to perform learning the input image and the correct answer data, and outputs a result thereof as a new learning discriminator to each terminal device.