Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: Systems and methods for assessing quality of and detecting defects in retail products include a housing having an interior, a product holding surface, an image capture device configured to capture an image of the product, a lighting element configured to provide lighting within the housing, an electronic database configured to store electronic data including reference image data associated with the product and representing the product in various sizes and various quality states, and a processor-based control circuit in communication with the first image capture device and the electronic database. The control circuit is configured to obtain image data representing the image of the product, obtain the reference image data, analyze the image data and the reference image data to identify the product and identify a size of a defect present on the product, and output a notification indicating whether or not the product is of acceptable quality.

Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.

Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.