IP Library › Granted Patent US 12,725,419
Granted Patent B2
US 12,725,419 · App. 18/623,560 · Granted Sep 1, 2026

Detecting apparatus, position calculation system, and detecting method

Inventors: Kento Iwahori (Nagoya, JP); Daiki Yokoyama (Miyoshi, JP)
Assignee: TOYOTA JIDOSHA KABUSHIKI KAISHA
G06V20/52G06T5/80G06T7/73G06V10/22G06V10/242G06V10/764G06V10/774G06V20/70G06T2207/20081G06T2207/30108G06T2207/30248G06V2201/08
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Quick Facts
Patent No.
US 12,725,419
App. No.
18/623,560
Granted
Sep 1, 2026
Kind
B2
Abstract

In a detecting apparatus configured to detect a vehicle in a captured image, the vehicle is configured to move in a factory in which manufacturing steps are performed to manufacture and ship the vehicle, the vehicle is classified into states in accordance with an appearance of the vehicle, varying among the manufacturing steps. The detecting apparatus includes a first processor configured to acquire the captured image, acquire state information indicating one of the states of the vehicle in the captured image, from among first detection models that are machine learning models each prepared one by one for the states, acquire the first detection model selected in accordance with the one of the states, identified by the acquired state information, and detect the vehicle in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle in the captured image.

Claims (51)

1 . A detecting apparatus configured to detect a vehicle contained in a captured image,

the vehicle being configured to move in a factory in which a plurality of manufacturing steps is performed to manufacture and ship the vehicle,

the vehicle being classified into a plurality of states in accordance with an appearance of the vehicle, the appearance varying among the plurality of manufacturing steps,

the detecting apparatus comprising a first processor configured to:

acquire the captured image;

acquire state information indicating one of the states of the vehicle contained in the captured image;

from among a plurality of first detection models that are machine learning models each prepared one by one for the states, acquire the first detection model selected in accordance with the one of the states, identified by the acquired state information; and

detect the vehicle contained in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle in the captured image,

wherein each of the plurality of first detection models is trained in advance to identify the target region by inputting a plurality of training images, and the plurality of training images includes M (M is an integer greater than or equal to two) first training images each containing the vehicle that is classified to the one of the states and N (N is an integer greater than or equal to zero and less than M) second training images each containing the vehicle that is classified to another one of the states, different from the one of the states.

2 . The detecting apparatus according to claim 1 , wherein a region correct label is associated with each region in the training image, and the region correct label indicates which one of the target region and an off-target region representing an object other than the vehicle.

3 . The detecting apparatus according to claim 1 , wherein the first processor is configured to acquire the state information by acquiring step information on one of the manufacturing steps, being performed on the vehicle, and identifying the state information associated with the one of the manufacturing steps, identified by the acquired step information by using a step database in which the state information is associated with each of the plurality of manufacturing steps.

4 . The detecting apparatus according to claim 1 , wherein the first processor is configured to, when the captured image is input, acquire the state information by inputting the captured image to a specific model that is a machine learning model trained so as to output the state information.

5 . The detecting apparatus according to claim 1 , wherein the first processor is configured to acquire the state information by acquiring image capture information on image capture devices and identifying the state information associated with one of the image capture devices, identified by the acquired image capture information, by using a state database in which the image capture information is associated with the state information.

6 . The detecting apparatus according to claim 1 , wherein:

the plurality of manufacturing steps includes painting the vehicle, and

the plurality of states includes an unpainted state and a painted state, the unpainted state indicates the state of the vehicle before being painted in painting the vehicle, the painted state indicates the state of the vehicle after being painted in painting the vehicle.

7 . The detecting apparatus according to claim 1 , wherein the first processor is configured to correct a distortion of the captured image.

8 . The detecting apparatus according to claim 1 , wherein the first processor is configured to rotate the captured image such that a moving direction of the vehicle is oriented in a predetermined direction.

9 . The detecting apparatus according to claim 1 , wherein

the plurality of states including a platform state, the platform state being a state of a platform in which the vehicle includes at least

a wheel,

a chassis,

a drive unit configured to accelerate the vehicle,

a steering device configured to change a traveling direction of the vehicle,

a braking device configured to decelerate the vehicle,

a vehicle controller configured to control an operation of the vehicle, and

a vehicle communication unit configured to communicate with another apparatus other than the host vehicle.

10 . A position calculation system configured to calculate a position of a vehicle contained in a captured image,

the vehicle being configured to move in a factory in which a plurality of manufacturing steps is performed to manufacture and ship the vehicle,

the vehicle being classified into a plurality of states in accordance with an appearance of the vehicle, the appearance varying among the plurality of manufacturing steps,

the position calculation system comprising:

a detecting apparatus configured to detect the vehicle contained in the captured image,

the detecting apparatus comprising a first processor configured to:

acquire the captured image;

acquire state information indicating one of the states of the vehicle contained in the captured image;

from among a plurality of first detection models that are machine learning models each prepared one by one for the states, acquire the first detection model selected in accordance with the one of the states, identified by the acquired state information; and

detect the vehicle contained in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle in the captured image; and

generate a first mask image, in which a mask region is added to the target region that is a region representing the vehicle, by masking the target region in the captured image; and

a position calculation apparatus configured to calculate a position of the vehicle, the position calculation apparatus including a second processor, the second processor being configured to

generate a second mask image by performing perspective transformation of the first mask image,

where a designated vertex of a first circumscribed rectangle set for the mask region in the first mask image is a first coordinate point and a vertex indicating the same position as the first coordinate point of a second circumscribed rectangle set for the mask region in the second mask image is a second coordinate point, calculate an image coordinate point indicating a position of the vehicle in an image coordinate system by correcting the first coordinate point using the second coordinate point, and

convert the image coordinate point to a vehicle coordinate point indicating a position of the vehicle in a global coordinate system by using a distance from a reference point of the image capture device, calculated based on a position of the image capture device in the global coordinate system, and a distance from the reference point of a predetermined measuring point of the vehicle.

11 . A detecting method of detecting a vehicle contained in a captured image,

the vehicle being configured to move in a factory in which a plurality of manufacturing steps is performed to manufacture and ship the vehicle,

the vehicle being classified into a plurality of states in accordance with an appearance of the vehicle, the appearance varying among the plurality of manufacturing steps,

the detecting method comprising:

acquiring the captured image;

acquiring state information indicating one of the states of the vehicle contained in the captured image;

from among a plurality of first detection models that are machine learning models each prepared one by one for the states, acquiring the first detection model selected in accordance with the one of the states, identified by the acquired state information; and

detecting the vehicle contained in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle in the captured image,

wherein each of the plurality of first detection models is trained in advance to identify the target region by inputting a plurality of training images, and the plurality of training images includes M (M is an integer greater than or equal to two) first training images each containing the vehicle that is classified to the one of the states and N (N is an integer greater than or equal to zero and less than M) second training images each containing the vehicle that is classified to another one of the states, different from the one of the states.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 1, 2024
From: IWAHORI, KENTO; YOKOYAMA, DAIKI
To: TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 066967/0559 →
Priority Claims (1)
JP 2023-099795 · Jun 19, 2023 · national
Continuity (1)
Related Publication 20240420473A1 · Dec 19, 2024
References Cited (17)
US 10552686B2 · Yasunaga · 2020 [cited by examiner]
US 10713507B2 · Noda · 2020 [cited by examiner]
US 10860870B2 · Noda · 2020 [cited by examiner]
US 10956782B2 · Sivalingam · 2021 [cited by examiner]
US 11132560B2 · Friedmann · 2021 [cited by examiner]
US 11189048B2 · Nishimura · 2021 [cited by examiner]
US 11680801B2 · Benou · 2023 [cited by examiner]
US 12283110B2 · Itsumi · 2025 [cited by examiner]
US 12354368B2 · Yang · 2025 [cited by examiner]
US 20170320529A1 · Nordbruch · 2017 [cited by applicant]
US 20200368861A1 · Artigas et al. · 2020 [cited by applicant]
US 20220129675A1 · Takimoto et al. · 2022 [cited by applicant]
US 20240420473A1 · Iwahori · 2024 [cited by examiner]
JP 2017538619A · 2017 [cited by applicant]
Yang, (CN 106248394 A an On-line Detecting Device for Automobile Air Outlet and Detecting Method Thereof), date published Dec. 21, 2016. [cited by examiner]
Bauer et al. (WO 2021043419 A1 Method and Device for Operating an Automation System), date published Mar. 11, 2021. [cited by examiner]
Wei et al. (CN 102534461 AProcess for Remanufacturing Engine Crankshaft by Automatic High-speedElectric Arc Spraying), date published Jul. 4, 2012. [cited by examiner]