Self-service checkout counter checkout

A computer-implemented system for self-service checkout, comprising:

• one or more computers;
• and
• one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations, comprising:
• obtaining, by using a camera, an image, wherein the image is of at least one product placed on a checkout counter;
• performing image segmentation on the image to obtain at least one image region;
• identifying a product code included in a code region in an image region of the at least one image region;
• determining that a product category of a product associated with the product code fails to be identified;
• in response to determining that the product category fails to be identified, determining the product category by using an object detection model obtained by pre-training using labeled training sample images, each labeled training sample image comprising a rectangle that frames the product code to label the code region, wherein the object detection model generates a candidate image region in the image, performs target identification within the candidate image region, performs bounding box regression in the candidate image region, and corrects the code region by using a perspective transformation relative to position markers to obtain a corrected code region, and determines the product category based on the corrected code region;
• and
• determining a price of the product based on the product category.

A non-transitory, computer-readable medium storing one or more instructions executable by a computer-implemented system to perform one or more operations for self-service checkout comprising:

• obtaining, by using a camera, an image, wherein the image is of at least one product placed on a checkout counter;
• performing image segmentation on the image to obtain at least one image region;
• identifying a product code included in a code region in an image region of the at least one image region;
• determining that a product category of a product associated with the product code fails to be identified;
• in response to determining that the product category fails to be identified, determining the product category by using an object detection model obtained by pre-training using labeled training sample images, each labeled training sample image comprising a rectangle that frames the product code to label the code region, wherein the object detection model generates a candidate image region in the image, performs target identification within the candidate image region, performs bounding box regression in the candidate image region, and corrects the code region by using a perspective transformation relative to position markers to obtain a corrected code region, and determines the product category based on the corrected code region;
• and
• determining a price of the product based on the product category.