# US 12,602,810 B2 · Tire-size identification method, tire-size identification system and computer-readable storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,810 B2 - Document ID: US12602810B2 - Status: Granted - Kind: B2 - Application number: 18/193,531 - Filed: 2023-03-30 - Published: 2024-06-06 - Granted: 2026-04-14 - Earliest claimed: 2022-12-06 - Assignees: Wistron Corporation - Applicants: Wistron Corp. - Inventors: Zhe-Yu Lin; Chih-Yi Chien - Web page: https://patentgrep.com/patent/US12602810B2 ## CPC - G06T 7/00: Image analysis - G06T 7/62: of area, perimeter, diameter or volume - G06T 2207/10016: Video; Image sequence - G06T 2207/20076: Probabilistic image processing - G06T 2207/30242: Counting objects in image - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A tire-size identification method includes the following steps. An object-detection model frames a tire image in an image to generate a framed tire image. The framed tire image is input into an image-segmentation model. The image-segmentation model outputs the tire inner diameter of the tire. Moreover, the tire inner diameter is input into a classification model. The classification model outputs the tire size. ## Claims ### Claim 1 A tire-size identification method, comprising: inputting an image into an object-detection model, wherein the object-detection model frames a tire image in the image to generate a framed tire image; inputting the framed tire image into an image-segmentation model, wherein the image-segmentation model outputs a tire inner diameter of the framed tire image; inputting the tire inner diameter into a classification model, wherein the classification model outputs a tire size according to an inner diameter pixel value of the tire inner diameter for classifying; and after inputting the inner diameter pixel value of the tire inner diameter into the classification model, the classification model outputting a probability corresponding to each of a plurality of candidate tire-size categories, and using a tire-size category corresponding to a highest probability of the probabilities as the tire size. ### Claim 2 (depends on 1) The tire-size identification method as claimed in claim 1, further comprising: using a processor to calculate a distance between the framed tire image in the image and the framed tire image in a next frame image, and to compare the framed tire image in the image and the framed tire image in the next frame image with a shortest distance between the image and the next frame image, so as to track a position of the framed tire image. ### Claim 3 (depends on 2) The tire-size identification method as claimed in claim 2, further comprising: using the processor to find a maximum value of the tire inner diameter from an original video; using a storage device to record the maximum value of the tire inner diameter in the original video; wherein the original video comprises the image and the next frame image. ### Claim 4 (depends on 1) The tire-size identification method as claimed in claim 1, further comprising: using a processor to count tires with the same tire size according to the tire size of each tire. ### Claim 5 (depends on 1) The tire-size identification method as claimed in claim 1, further comprising: superimposing obtained information about the tire size on an original video, and transmitting a video superimposed with information about the tire size to a display device for display. ### Claim 6 (depends on 1) The tire-size identification method as claimed in claim 1, further comprising: obtaining the image, wherein the image is a region-of-interest image. ### Claim 7 A tire-size identification system, comprising: a processor; and a storage device, wherein the processor accesses a program stored in the storage device to implement an object-detection model, an image-segmentation model and a classification model; wherein the object-detection model frames a tire image in an image to generate a framed tire image; wherein the image-segmentation model outputs a tire inner diameter of the framed tire image; wherein the classification model outputs a tire size according to an inner diameter pixel value of the tire inner diameter for classifying; and wherein after the processor inputs the inner diameter pixel value of the tire inner diameter into the classification model, the classification model outputs a probability corresponding to each of a plurality of candidate tire-size categories and uses a tire-size category corresponding to a highest probability of the probabilities as the tire size. ### Claim 8 (depends on 7) The tire-size identification system as claimed in claim 7, wherein the processor calculates a distance between the framed tire image in the image and the framed tire image in a next frame image, and compares the framed tire image in the image and the framed tire image in the next frame image with a shortest distance between the image and the next frame image, so as to track a position of the framed tire image. ### Claim 9 (depends on 8) The tire-size identification system as claimed in claim 8, wherein the processor finds a maximum value of the tire inner diameter from an original video, and the storage device records the maximum value of the tire inner diameter in the original video; wherein the original video comprises the image and the next frame image. ### Claim 10 (depends on 7) The tire-size identification system as claimed in claim 7, wherein the processor counts tires with the same tire size according to the tire size of each tire. ### Claim 11 (depends on 7) The tire-size identification system as claimed in claim 7, wherein the processor superimposes obtained information about the tire size on an original video, and transmits a video superimposed with information about the tire size to a display device for display. ### Claim 12 (depends on 7) The tire-size identification system as claimed in claim 7, wherein the processor further obtains the image, wherein the image is a region-of-interest image. ### Claim 13 A non-transitory computer-readable storage medium that stores one or more programs comprising instructions that, when executed by a processor, cause the processor to perform the following steps: inputting an image into an object-detection model, wherein the object-detection model frames a tire image in the image to generate a framed tire image; inputting the framed tire image into an image-segmentation model, wherein the image-segmentation model outputs a tire inner diameter of the framed tire image; inputting the tire inner diameter into a classification model, wherein the classification model outputs a tire size according to an inner diameter pixel value of the tire inner diameter for classifying; and after inputting the inner diameter pixel value of the tire inner diameter into the classification model, the classification model outputting a probability corresponding to each of a plurality of candidate tire-size categories, and using a tire-size category corresponding to a highest probability of the probabilities as the tire size. ### Claim 14 (depends on 13) The non-transitory computer-readable storage medium as claimed in claim 13, wherein when the instructions are executed by the processor, the processor is caused to further perform the following steps: calculating a distance between the framed tire image in the image and the framed tire image in a next frame image, and comparing the framed tire image in the image and the framed tire image in the next frame image with a shortest distance between the image and the next frame image, so as to track a position of the framed tire image. ### Claim 15 (depends on 14) The non-transitory computer-readable storage medium as claimed in claim 14, wherein when the instructions are executed by the processor, the processor is caused to further perform the following steps: finding a maximum value of the tire inner diameter from an original video; recording the maximum value of the tire inner diameter in the original video; wherein the original video comprises the image and the next frame image. ### Claim 16 (depends on 13) The non-transitory computer-readable storage medium as claimed in claim 13, wherein when the instructions are executed by the processor, the processor is caused to further perform the following steps: counting tires with the same tire size according to the tire size of each tire. ### Claim 17 (depends on 13) The non-transitory computer-readable storage medium as claimed in claim 13, wherein when the instructions are executed by the processor, the processor is caused to further perform the following steps: superimposing obtained information about the tire size on an original video, and transmitting a video superimposed with information about the tire size to a display device for display.