Implementations of the subject matter described herein relate to object detection based on deep neural network. With a given input image, it is desired to determine a class and a boundary of one or more objects within the input image. Specifically, a plurality of channel groups is generated from a feature map of an image, the image including at least a region corresponding to a first grid. A target feature map is extracted from at least one of the plurality of channel groups associated with a cell of the first grid. Information related to an object within the region is determined based on the target feature map. The information related to the object may be a class and/or a boundary of the object.

An electronic apparatus is provided. The electronic apparatus includes a camera, a storage, and a processor configured to store an image photographed by the camera and metadata of the image in the storage, the processor is further configured to identify whether first information related to the image is obtainable, based on the first information not being obtainable, generate metadata related to the first information based on second information, and store the generated metadata as metadata of the image.

Methods and systems for associating labels between multiple sensors. One of the methods includes generating user interface presentation data that, when presented on a user device, causes the user device to display a user interface that: displays the images in the sequence of images and data identifying the first and second object tracks, and is configured to receive user inputs that associate first object tracks with second object tracks; and providing the user interface presentation data for presentation on the user device.

Techniques are disclosed for systems and methods to provide remote sensing imagery for mobile structures. A remote sensing imagery system includes a radar assembly mounted to a mobile structure and a coupled logic device. The radar assembly includes an imaging system coupled to or within the radar assembly and configured to provide image data associated with the radar assembly. The logic device is configured to receive radar returns corresponding to a detected target from the radar assembly and image data corresponding to the radar returns from the imaging system, and then generate radar image data based on the radar returns and the image data. Subsequent user input and/or the sensor data may be used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.

Techniques are disclosed for systems and methods to provide remote sensing imagery for mobile structures. A remote sensing imagery system includes a radar assembly mounted to a mobile structure and a coupled logic device. The radar assembly includes an imaging system coupled to or within the radar assembly and configured to provide image data associated with the radar assembly. The logic device is configured to receive radar returns corresponding to a detected target from the radar assembly and image data corresponding to the radar returns from the imaging system, and then generate radar image data based on the radar returns and the image data. Subsequent user input and/or the sensor data may be used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.

Various implementations disclosed herein include devices, systems, and methods that generate floorplans and measurements using a three-dimensional (3D) representation of a physical environment generated based on sensor data.

In one example embodiment, a computer-implemented method for extracting information from imagery includes obtaining data representing a sequence of images, at least one of the sequence of images depicting an object. The method includes inputting the sequence of images into a machine-learned information extraction model that is trained to extract location information from the sequence of images. The method includes obtaining as an output of the information extraction model in response to inputting the sequence of images, data representing a real-world location associated with the object depicted in the sequence of images.

Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

An information processing device comprises an electronic device, an averaging circuit acquiring output signals from the electronic device multiple times in a predetermined period and averaging the signals acquired multiple times, a memory circuit storing an averaged signal averaged by the averaging circuit and a PUF-ID extraction circuit generating a unique identifier based on the averaged signal.