Abstract: A gimbal servo control method includes obtaining a predicted flight trajectory of an aircraft within a preset time period from a current time, and adjusting an attitude of a gimbal carried by the aircraft according a predicted velocity direction of the aircraft at a point of the predicted flight trajectory.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may identify a product identifier associated with an object in a captured image; generate predicted product identifiers associated with the object in the captured image based on text identified from the object in the captured image; aggregate the predicted product identifiers; determine a feature of the objects associated with the aggregated predicted product identifiers; determine one or more confusing product identifiers based on a determination of the aggregated predicted product identifiers being associated with the feature; and update a dataset with at least one of the one or more confusing product identifiers and images associated with the one or more confusing product identifiers.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model; and a control circuit. The control circuit may group a plurality of product identifiers into one or more clusters based on at least one of visual similarity of corresponding images, textual similarity of corresponding associated descriptions, and associated relationships between product identifiers of the plurality of product identifiers; determine clusters having common elements that are at least within a similarity threshold of each other; merge the clusters with the common elements; and generate a mapping dataset used to retrain the trained machine learning model to identify a plurality of objects. The mapping dataset may include a plurality of associations of associated product identifiers to a single object.

Abstract: Systems and methods of pairing product labels with products located on a product storage structure of a product storage facility include an image capture device that captures one or more images of the product storage structure and a computing device that obtains images of the product storage structure captured by the image capture device, analyzes the obtained images to detect product labels and products located on the product storage structure, and crops the detected individual products and individual price tag labels from the images to generate cropped images. Then the computing device stitches the cropped price tag label and product images, receives one or more characters extracted from the portions of the stitched images corresponding to the cropped images, and associates, based on known positional coordinates of the products and product labels in the stitched images, the received extracted characters with corresponding cropped images of the products and product labels.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects at a product storage facility including a trained machine learning model stored in a memory; and a control circuit. The control circuit may obtain an image at the product storage facility; cluster objects depicted in the image that have same product identifiers into a corresponding group; determine coordinates of each bounding box of each clustered object in the corresponding group; determine a bounding box representative depth value of pixels inside the bounding box of each clustered object; determine an overall representative depth value of the corresponding group based on bounding box representative depth values of clustered objects; and exclude the clustered objects from identified objects in the image upon a determination that the overall representative depth value is greater than a threshold.

Abstract: Systems and methods of updating templates for use in recognizing individual products in images captured at a product storage facility include an image capture device that captures one or more images of product storage structure at a product storage facility, a computing device in communication with the image capture device, and an electronic database that stores keyword model templates and feature model templates associated with images of previously recognized individual products detected at the product storage facility.

Abstract: Systems and methods of detecting support members of product storage structures that store products at a product storage facility include an image capture device that captures images of a product storage structure including vertical and horizontal support members. A computing device including a control circuit is configured to: obtain the images of the product storage structure captured by the image capture device, stitch the obtained images together to generate a stitched image that depicts the product storage structure, generate a color distribution map of the stitched image of the product storage structure to detect individual ones of the horizontal and vertical support members of the product storage structure.

Abstract: Systems and methods of creating reference template images for detecting and recognizing products at a product storage facility include an image capture device having a field of view that includes a product storage structure of the product storage facility, and a computing device including a control circuit and being communicatively coupled to the image capture device. The computing device obtains images of the product storage structure captured by the image capture device, analyzes the obtained images to detect individual ones of the products located on the product storage structure. Then, the computing device identifies the individual ones of the products detected in the images and crops each of the individual ones of the identified products from the images to generate cropped images. The computing device then creates a cluster of the cropped images, and selects one of the cropped images as a reference template image of an identified individual product.

Abstract: Systems and methods of detecting and recognizing products on product storage structures of a product storage facility include an image capture device that moves about and captures images of the product storage structures at the product storage facility. A computing device processes the obtained images to detect and identify the products on the product storage structure, crops each of the identified individual products from the image to generate a plurality of cropped images and generates an image histogram template, feature vector template and location information template for each of the cropped images. The cropped images are stored in an electronic database and represent a reference model for each of the identified individual products and are stored in association with the generated image histogram template, feature vector template and location information template to facilitate recognition of products subsequently captured on the product storage structure by the image capture device.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to processing captured images of objects at a product storage facility. In some embodiments, there is provided a system for processing captured images of objects including a trained machine learning model and a control circuit. In some embodiments, the trained machine learning model is configured to process unprocessed captured images. In some embodiments, the control circuit is configured to associate each of the processed images into one of a first group, a second group, or a third group; remove at least one processed image associated with the first group from the processed images in accordance with a first processing rule; and output remaining processed images associated with the first group and processed images associated with the second group to be used to retrain the trained machine learning model.

Abstract: The present disclosure provides a tea plant CsVAAT3 gene and use thereof. The tea plant CsVAAT3 gene has a nucleotide sequence shown in SEQ ID NO: 1 in sequence listing. A protein coded by the tea plant CsVAAT3 gene has an amino acid sequence shown in SEQ ID NO: 2 in the sequence listing. An expression pattern of CsVAAT3 in tea plant is significantly positively correlated with the nitrogen level in tea roots. The CsVAAT3 is highly expressed in tea roots. A pDR196-CsVAAT3 plasmid constructed from this gene is transformed into a vacuolar amino acid uptake-deficient yeast strain ypq2, which can restore the growth ability of the yeast mutant on a high-concentration theanine medium. Cloning of the gene is beneficial to analysis of a molecular mechanism of theanine storage in tea roots, providing an important target gene resource and a theoretical basis for cultivation of new tea cultivars with high theanine content.

Abstract: The present disclosure provides a tea plant CsFAAH6 gene and use thereof. The tea plant CsFAAH6 gene has a nucleotide sequence shown in SEQ ID NO: 1 in sequence listing. A protein coded by the tea plant CsFAAH6 gene has an amino acid sequence shown in SEQ ID NO: 2 in the sequence listing. The CsFAAH6 is highly expressed in mature leaves of tea plants; there is significant negative correlation between theanine content in shoots (one bud and two leaves) of different tea cultivars in different months and expression level of the CsFAAH6; instantaneous silencing of CsFAAH6 expression can significantly increase the theanine content, indicating that CsFAAH6 has a physiological function of theanine degradation and a molecular mechanism thereof.