KR-20260067230-A - BCR camera and method for recognizing barcode

KR20260067230AKR 20260067230 AKR20260067230 AKR 20260067230AKR-20260067230-A

Abstract

The present invention provides a method for recognizing a barcode using a BCR camera installed in a conveyor environment, comprising the steps of: acquiring a barcode image of a barcode attached to a cargo on a conveyor using the BCR camera; calculating a field of view of the BCR camera based on conveyor environment conditions and BCR camera setting conditions; calculating a distance traveled by the cargo on the conveyor using the field of view, the conveyor environment conditions, and the BCR camera setting conditions; generating a blur kernel based on the direction of movement of the cargo and the distance traveled; and deblurring the barcode image using a pre-learned deblurring algorithm based on the blur kernel.

Inventors

정수현
김창현

Assignees

한화비전 주식회사

Dates

Publication Date: 20260512
Application Date: 20241105

Claims (10)

In a method for recognizing barcodes using a BCR camera installed in a conveyor environment, A step of acquiring a barcode image of a barcode attached to cargo on a conveyor using a BCR camera; A step of calculating the field of view of the BCR camera based on conveyor environment conditions and BCR camera setting conditions; A step of calculating the distance traveled by a cargo on a conveyor using the above-mentioned field of view area, the above-mentioned conveyor environment conditions, and the above-mentioned BCR camera setting conditions; A step of generating a blur kernel based on the direction of movement of the cargo and the distance of movement; and A step of deblurring the barcode image using a pre-trained deblurring algorithm based on the above blur kernel; A barcode recognition method including
In Article 1, The step of calculating the field of view of the above BCR camera is, A step of calculating a horizontal axis field of view and a vertical axis field of view based on the installation height of the BCR camera from the conveyor, the sensor size of the BCR camera, and the focal length of the BCR camera; and A barcode recognition method comprising the step of calculating a horizontal axis field of view area and a vertical axis field of view area based on the horizontal axis field of view angle and the vertical axis field of view angle.
In Article 2, The step of calculating the above travel distance is, A step of calculating the actual travel distance according to a single frame of the cargo based on the speed of the conveyor and the shutter speed of the BCR camera; and A barcode recognition method comprising the step of converting the actual travel distance into a travel distance according to a single pixel.
In Paragraph 3, The step of generating the above blur kernel is, A step of calculating a motion vector based on the distance traveled according to the single pixel and the direction of movement of the cargo; and A barcode recognition method comprising the step of generating a blur kernel based on the above motion vector.
In Article 1, A barcode recognition method comprising the step of deblurring the above barcode image based on the blur kernel and the barcode image using a Wiener filter algorithm.
In a BCR camera installed in a conveyor environment, A sensor for acquiring a barcode image of a barcode attached to cargo on a conveyor; and Includes a processor, A BCR camera, wherein the processor calculates the field of view of the BCR camera based on conveyor environment conditions and BCR camera setting conditions, calculates the distance traveled by a cargo on a conveyor using the field of view, the conveyor environment conditions, and the BCR camera setting conditions, generates a blur kernel based on the direction of movement of the cargo and the distance traveled, and deblurs the barcode image using a pre-learned deblurring algorithm based on the blur kernel.
In Article 6, A BCR camera, wherein the processor calculates a horizontal axis field of view and a vertical axis field of view based on the installation height of the BCR camera from the conveyor, the sensor size of the BCR camera, and the focal length of the BCR camera, and calculates a horizontal axis field of view area and a vertical axis field of view area based on the horizontal axis field of view and the vertical axis field of view.
In Article 7, The processor calculates the actual distance traveled per single frame of the cargo based on the speed of the conveyor and the shutter speed of the BCR camera, and converts the actual distance traveled per single pixel into a distance traveled per pixel.
In Article 8, A BCR camera, wherein the processor calculates a motion vector based on the distance traveled according to the single pixel and the direction of movement of the cargo, and generates a blur kernel based on the motion vector.
In Article 6, The above processor is a BCR camera that deblurs the barcode image based on the blur kernel and the barcode image using a Wiener filter algorithm.

Description

Method for recognizing barcodes and BCR camera Embodiments of the present invention relate to a method for recognizing barcodes and a BCR camera. BCR cameras are utilized to enhance logistics automation and operational reliability by accurately recognizing barcodes even in high-speed conveyor environments. However, accurate barcode recognition requires high-resolution images with minimal blur. Generally, PPM (Pixels Per Module), a key indicator of a barcode reader's resolution, refers to the number of pixels per barcode module. Conversely, if the barcode is blurry, it becomes difficult to distinguish modules—the smallest unit width composing the barcode—thereby increasing the difficulty of recognition. Conventional techniques optimize deconblation by assuming only camera blur occurring within the camera optical system for the blur kernel present in barcode images, or by analyzing the difference in features between a sharp barcode and a deblurred barcode when motion blur is assumed. However, these methods suffer from long estimation times because they require the execution of algorithms to iteratively find the optimal blur kernel. Furthermore, they are limited to systems on mobile terminal devices and do not assume a fixed camera. Consequently, estimating the blur kernel is challenging because the movement speed is not constant, and the direction of movement must account for camera movement as well as object movement. FIG. 1 is a diagram illustrating the operation of a BCR camera that recognizes barcodes according to an embodiment of the present invention. FIG. 2 is a drawing for explaining the configuration and operation of a BCR camera according to one embodiment of the present invention. FIG. 3 is a flowchart illustrating a barcode recognition method according to an embodiment of the present invention. FIG. 4 is a flowchart illustrating a barcode recognition method according to another embodiment of the present invention. FIGS. 5 and 6 are drawings for explaining a barcode recognition method according to an embodiment of the present invention. The present invention is capable of various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention, and the methods for achieving them, will become clear by referring to the embodiments described below in detail together with the drawings. However, the present invention is not limited to the embodiments disclosed below but can be implemented in various forms. Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. When describing with reference to the drawings, identical or corresponding components are given the same reference numerals, and redundant descriptions thereof will be omitted. In the following embodiments, terms such as "first," "second," etc. are used not in a limiting sense, but for the purpose of distinguishing one component from another. Also, singular expressions include plural expressions unless the context clearly indicates otherwise. Furthermore, terms such as "include" or "have" mean that the feature or component described in the specification exists, and do not exclude the possibility that one or more other features or components may be added. In the drawings, the size of components may be exaggerated or reduced for convenience of explanation. For example, the size and thickness of each component shown in the drawings are depicted arbitrarily for convenience of explanation, so the present invention is not necessarily limited to what is illustrated. In the following embodiments, when a part such as a region, component, section, block, or module is described as being on or above another part, it includes not only cases where it is directly on top of the other part, but also cases where another region, component, section, block, or module is interposed therein. Furthermore, when a region, component, section, block, or module is described as being connected, it includes not only cases where the region, component, section, block, or module is directly connected, but also cases where other regions, components, sections, blocks, or modules are interposed therein to indirectly connect them. Hereinafter, in order to enable a person skilled in the art to easily practice the present invention, various embodiments of the present invention will be described in detail with reference to the attached drawings. FIG. 1 is a diagram illustrating the operation of a BCR camera that recognizes barcodes according to an embodiment of the present invention. FIG. 2 is a diagram illustrating the configuration and operation of a BCR camera according to an embodiment of the present invention. Referring to FIG. 1 and FIG. 2 together, a barcode recognition system according to one embodiment of the present invention may include a BCR camera (100). However, the present