Abstract: Computer vision systems and methods for hazard detection from digital images and videos are provided. The system obtains media content indicative of an asset, preprocesses the media content, and extracts, based at least in part on one or more feature extractors, features from the preprocessed media content. The system determines, based at least in part on one or more classifiers, a value associated with a hazard, which can indicate the likelihood of a media content having the hazard. The system determines that the media content includes the hazard based at least in part on a comparison of the value to a threshold value. The system can generate a visual indication in the image indicative of the hazard.

Abstract: Systems and methods for detecting artificial intelligence generated images are provided. The system accepts an input image (e.g., a digital still image, or a frame from a digital video file or image stream) and subdivides the input image into a set of patches using a patch partitioning algorithm. The system then processes each patch and produces a feature embedding for each patch within a high dimension space. The system then utilizes these patches with further processing as input to machine learning models, which allows the system to achieve image, patch-level, and video-frame generated image classification and localization alongside identification of the generative model used to synthesize the image.

Abstract: Computer vision systems and methods for hazard detection from digital images and videos are provided. The system obtains media content indicative of an asset, preprocesses the media content, and extracts, based at least in part on one or more feature extractors, features from the preprocessed media content. The system determines, based at least in part on one or more classifiers, a value associated with a hazard, which can indicate the likelihood of a media content having the hazard. The system determines that the media content includes the hazard based at least in part on a comparison of the value to a threshold value. The system can generate a visual indication in the image indicative of the hazard.

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: Machine learning systems and methods for validating workflows are provided. The system receives one or more digital images of a work action being performed, and processes the image using a hierarchical modeling process that includes a filter model and a cascaded expert model. The filter model processes the image to ascertain whether the image is suitable for use in validating completion of the work action. If the system determines that the image is suitable, the image is then processed by the expert model to classify whether the image depicts a scene occurring before, during, or after performance of the work action. The hierarchical modeling process can be utilized to validate different workflows based upon a specified workflow type. A plurality of hierarchical modeling processes can be applied in parallel to an input image in the event that a type of workflow to be validated is not specified in advance.

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: Computer vision systems and methods for property scene understanding from digital images, videos, media and/or sensor information are provided. The system obtains media content indicative of an asset, performs feature segmentation and material recognition, performs object detection on the features, performs hazard detection to detect one or more safety hazards, and performs damage detection to detect any visible damage, to develop a better understanding of the property using one or more features in the media content. The system can output the feature segmentation and material detection, the hazard detection, the content feature detection, and the damage detection, and all other available models to an adjuster or other user on a user interface.

Abstract: Computer vision systems and methods for hazard detection from digital images and videos are provided. The system obtains media content indicative of an asset, preprocesses the media content, and extracts, based at least in part on one or more feature extractors, features from the preprocessed media content. The system determines, based at least in part on one or more classifiers, a value associated with a hazard, which can indicate the likelihood of a media content having the hazard. The system determines that the media content includes the hazard based at least in part on a comparison of the value to a threshold value. The system can generate a visual indication in the image indicative of the hazard.

Abstract: A system and method for monitoring a communications network having a plurality of wired communication cables is characterized by a central computer and a plurality of remote sensors connected with the cables at spaced locations for sampling data at the locations and providing an indication when a fault occurs. The central computer transmits measurement request signals to each remote sensor. In response to a request, the remote sensor samples data on the network at the unit location and sends a measurement of the sample to the central computer where the measurement is compared with baseline measurements for the location. If the measured data from the unit deviates from the baseline measurement, a fault is indicated.

Abstract: A system and method for monitoring a communications network having a plurality of wired communication cables is characterized by a central computer and a plurality of remote sensors connected with the cables at spaced locations for sampling data at the locations and providing an indication when a fault occurs. The central computer transmits measurement request signals to each remote sensor. In response to a request, the remote sensor samples data on the network at the unit location and sends a measurement of the sample to the central computer where the measurement is compared with baseline measurements for the location. If the measured data from the unit deviates from the baseline measurement, a fault is indicated.