An intelligent item receptacle, system, and method for providing supplemental media content in response to depositing an item having media indicia disposed thereon. An intelligent item receptacle includes an item receptacle securely enclosing an interior volume, an item sensor configured to detect an inserted item, a camera within the interior volume configured to capture an image of the item, and an output configured to play audio or visual content to bystanders outside the item receptacle. Processing circuitry within the item receptacle analyzes the image to determine if the media indicia are present on the item and, if present, causes the output to play the audio or visual content associated with the media indicia.

A detection system (100) is disclosed herein. The system includes a sensor system (120) positioned to obtain image sensor data at different times of a live cannabis plant and a data storage system (130) configured to store the image sensor data. The system further includes a processor (140) coupled to the data storage system to receive the image sensor data. The processor includes a target region selection module (160) configured to determine a region of the live cannabis plant that contains a flower and generate a feature indicative of a characteristic of the flower. The processor further includes a grade estimation module (170) configured to estimate a qualitative assessment for the flower based on the feature and a temporal aggregation module (540) configured to combine the estimated qualitative assessments to output a final aggregated assessment.

A system for quantitively determining quality for pavement markings disposed along pavement on a roadway includes one or more controllers in wireless communication with a plurality of vehicles. The one or more controllers receive image data represents the pavement markings disposed along the pavement collected by the plurality of vehicles. The one or more controllers execute instructions to determine at least one of a color distance measurement between a mean color space value of the pavement markings and an ideal marking color space value and a marking intensity contrast ratio between the pavement markings and the pavement.

A system for quantitively determining quality for pavement markings disposed along pavement on a roadway includes one or more controllers in wireless communication with a plurality of vehicles. The one or more controllers receive image data represents the pavement markings disposed along the pavement collected by the plurality of vehicles. The one or more controllers execute instructions to determine at least one of a color distance measurement between a mean color space value of the pavement markings and an ideal marking color space value and a marking intensity contrast ratio between the pavement markings and the pavement.

The present disclosure relates to methods and apparatus for image processing. The apparatus can generate object mask information for one or more objects in a first image of a plurality of images in a scene. In some aspects, the first image can be at least one of a downscaled image, a down-sampled image, or a low resolution image. The apparatus can also determine one or more object classifications of the first image based on the generated object mask information. Additionally, the apparatus can identify a modification to at least one of the one or more object classifications based on a second image of the plurality of images in the scene. In some aspects, the apparatus can adjust or maintain the one or more object classifications based on the identified modification to at least one of the one or more object classifications.

The present disclosure relates to methods and apparatus for image processing. The apparatus can generate object mask information for one or more objects in a first image of a plurality of images in a scene. In some aspects, the first image can be at least one of a downscaled image, a down-sampled image, or a low resolution image. The apparatus can also determine one or more object classifications of the first image based on the generated object mask information. Additionally, the apparatus can identify a modification to at least one of the one or more object classifications based on a second image of the plurality of images in the scene. In some aspects, the apparatus can adjust or maintain the one or more object classifications based on the identified modification to at least one of the one or more object classifications.

A mask structure optimization device includes a classification target image size acquisition unit that is configured to acquire a size of a classification target image which is an image including a classification target, a mask size setting unit that is configured to set a size of a mask applied to the classification target image, a brightness detection unit that is configured to detect a brightness of each pixel within the classification target image at a position on an opposite side of the mask from the classification target image, a sum total brightness calculation unit that is configured to calculate the sum total brightness of the each pixel within the classification target image detected by the brightness detection unit, an initial value setting unit that is configured to set an initial value for a mask pattern of the mask, and a movement unit that is configured to relatively move the mask with respect to the classification target image. The sum total brightness calculation unit is configured to calculate the sum total brightness of the each pixel within the classification target image every time the movement unit relatively moves the mask by a predetermined movement amount. The mask structure optimization device further includes a mask pattern optimization unit that is configured to optimize the mask pattern of the mask on the basis of the sum total brightness.

A mask structure optimization device includes a classification target image size acquisition unit that is configured to acquire a size of a classification target image which is an image including a classification target, a mask size setting unit that is configured to set a size of a mask applied to the classification target image, a brightness detection unit that is configured to detect a brightness of each pixel within the classification target image at a position on an opposite side of the mask from the classification target image, a sum total brightness calculation unit that is configured to calculate the sum total brightness of the each pixel within the classification target image detected by the brightness detection unit, an initial value setting unit that is configured to set an initial value for a mask pattern of the mask, and a movement unit that is configured to relatively move the mask with respect to the classification target image. The sum total brightness calculation unit is configured to calculate the sum total brightness of the each pixel within the classification target image every time the movement unit relatively moves the mask by a predetermined movement amount. The mask structure optimization device further includes a mask pattern optimization unit that is configured to optimize the mask pattern of the mask on the basis of the sum total brightness.

The present invention discloses a cloud-edge-end cooperative control method of a 5G networked UAV for security rescue, including: an image acquisition step: performing, by a single-chip microcomputer, attitude resolution on data acquired by a detection sensor, to obtain image data; a sparse landmark map building step: performing, by a control platform, front-end feature point matching, local map building and optimization, loopback detection, and frame resolution on the image data, to generate a sparse landmark map; a three-dimensional dense map building step: generating, by an edge cloud, a three-dimensional dense map based on a key frame pose and key frame observation data of the sparse landmark map; a high-precision semantic map building step: obtaining a high-precision semantic map; and a UAV movement step: adjusting, by the driving mechanism, a pose of the UAV according to the three-dimensional dense map or the high-precision semantic map.

The present invention discloses a cloud-edge-end cooperative control method of a 5G networked UAV for security rescue, including: an image acquisition step: performing, by a single-chip microcomputer, attitude resolution on data acquired by a detection sensor, to obtain image data; a sparse landmark map building step: performing, by a control platform, front-end feature point matching, local map building and optimization, loopback detection, and frame resolution on the image data, to generate a sparse landmark map; a three-dimensional dense map building step: generating, by an edge cloud, a three-dimensional dense map based on a key frame pose and key frame observation data of the sparse landmark map; a high-precision semantic map building step: obtaining a high-precision semantic map; and a UAV movement step: adjusting, by the driving mechanism, a pose of the UAV according to the three-dimensional dense map or the high-precision semantic map.