Abstract: Methods and systems for providing automated asset management are provided. In embodiments, an asset data collector is configured to automatically collect asset data associated with assets in an intermodal facility according to a mission plan. An automated data collection configurator is configured to configure the mission plan based on one or more parameters and to instantiate the asset data collector to initiate collection of the asset data according to the mission plan. An automated asset data analyzer is configured to identify data in which one or more assets appear, to identify the one or more assets detected in the set of data, to inspect, based on the set of data in which the one or more assets are detected, the one or more assets to determine a condition of the one or more assets, and to output the condition of the one or more assets.

Abstract: Methods and systems for providing mechanisms for automated inventory control are provided. In embodiments, an operational workflow for providing automated inventory control includes automated data collection and automated image data analysis. The automated data collection includes capturing image data for a storage facility by a capturing device (e.g., an unmanned aerial system (UAS)). The automated image data analysis includes functionality to detect objects (e.g., containers, trailers, empty slots, and/or other objects) appearing in the image data (e.g., using a machine learning (ML) model), to identify the objects in the image data (e.g., using the ML model), to inspect the objects (e.g., including determining a condition and/or location of the objects) based on the collected image data and metadata associated with the image capturing device and correlated to the image data (e.g.

Abstract: Methods and systems for providing automated asset management are provided. In embodiments, an asset data collector is configured to automatically collect asset data associated with assets in an intermodal facility according to a mission plan. An automated data collection configurator is configured to configure the mission plan based on one or more parameters and to instantiate the asset data collector to initiate collection of the asset data according to the mission plan. An automated asset data analyzer is configured to identify data in which one or more assets appear, to identify the one or more assets detected in the set of data, to inspect, based on the set of data in which the one or more assets are detected, the one or more assets to determine a condition of the one or more assets, and to output the condition of the one or more assets.

Abstract: An on-board thermal track misalignment detection system method therefor is presented. The system can use on-board locomotive sensors attached to an end-of-train device to detect (on the edge), signs and symptoms of thermal misalignments of the track. Once detected an alert can be transmitted to prevent potential derailments. The system can also include a forward-facing and rearward-facing imaging sensors (e.g., camera, LiDAR sensor, etc). The system can wirelessly communicate (e.g., via radio) with a leading locomotive to ensure proper air pressure and location. The system can be powered by an on-board battery and/or air pressure device. Advantageously, the system can calculate whether any rail deviation is significant (e.g., via one or more threshold values). The system can also leverage image processing functionality, executed by one or more processors) to find the centerline and the distance between the tracks.

Abstract: An on-board thermal track misalignment detection system method therefor is presented. The system can use on-board locomotive sensors attached to an end-of-train device to detect (on the edge), signs and symptoms of thermal misalignments of the track. Once detected an alert can be transmitted to prevent potential derailments. The system can also include a forward-facing and rearward-facing imaging sensors (e.g., camera, LiDAR sensor, etc). The system can wirelessly communicate (e.g., via radio) with a leading locomotive to ensure proper air pressure and location. The system can be powered by an on-board battery and/or air pressure device. Advantageously, the system can calculate whether any rail deviation is significant (e.g., via one or more threshold values). The system can also leverage image processing functionality, executed by one or more processors) to find the centerline and the distance between the tracks.

Abstract: Methods and systems for providing mechanisms for automated inventory control are provided. In embodiments, an operational workflow for providing automated inventory control includes automated data collection and automated image data analysis. The automated data collection includes capturing image data for a storage facility by a capturing device (e.g., an unmanned aerial system (UAS)). The automated image data analysis includes functionality to detect objects (e.g., containers, trailers, empty slots, and/or other objects) appearing in the image data (e.g., using a machine learning (ML) model), to identify the objects in the image data (e.g., using the ML model), to inspect the objects (e.g., including determining a condition and/or location of the objects) based on the collected image data and metadata associated with the image capturing device and correlated to the image data (e.g.

Abstract: An on-board thermal track misalignment detection system method therefor is presented. The system can use on-board locomotive sensors attached to an end-of-train device to detect (on the edge), signs and symptoms of thermal misalignments of the track. Once detected an alert can be transmitted to prevent potential derailments. The system can also include a forward-facing and rearward-facing imaging sensors (e.g., camera, LiDAR sensor, etc). The system can wirelessly communicate (e.g., via radio) with a leading locomotive to ensure proper air pressure and location. The system can be powered by an on-board battery and/or air pressure device. Advantageously, the system can calculate whether any rail deviation is significant (e.g., via one or more threshold values). The system can also leverage image processing functionality, executed by one or more processors) to find the centerline and the distance between the tracks.