Systems and methods for controlling the disgorging of objects in containers by vibratory motion
US-11267662-B2 · Mar 8, 2022 · US
Insertion automation method and system based on deep learning parcel recognition
US12494061B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12494061-B2 |
| Application number | US-202318215925-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jun 29, 2023 |
| Priority date | Jul 7, 2022 |
| Publication date | Dec 9, 2025 |
| Grant date | Dec 9, 2025 |
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Abstract
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Disclosed are an insertion automation method and system based on deep-learning parcel recognition. The insertion automation method includes determining an initial gradient change angular velocity of a tipper based on a total weight of parcels in the tipper, recognizing a loading state of the parcels in the tipper by inputting images of the parcels in the tipper to an object recognition model, and redetermining a gradient change angular velocity and gradient angle of the tipper based on the recognized loading state, in which the loading state of the parcels in the tipper includes at least one of a position, size, and packing material of the parcels in the tipper.
First claim
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What is claimed is: 1 . An insertion automation method comprising: determining an initial gradient change angular velocity of a tipper based on a total weight of parcels in the tipper; recognizing a loading state of the parcels in the tipper by inputting images of the parcels in the tipper to an object recognition model; and redetermining a gradient change angular velocity of the tipper based on the recognized loading state, wherein the loading state of the parcels in the tipper comprises at least one of a position, size, and packing material of the parcels in the tipper. 2 . The insertion automation method of claim 1 , wherein the object recognition model comprises: a neural network trained to infer a packing material of each of parcels comprised in a training image in response to the training image that is predetermined being input. 3 . The insertion automation method of claim 1 , wherein the object recognition model comprises: a neural network trained to infer a size of parcels based on coordinate information of the parcels comprised in a training image in response to the training image that is predetermined being input. 4 . The insertion automation method of claim 1 , further comprising extracting a relative distance of the parcels in the tipper through heterogeneous sensors. 5 . The insertion automation method of claim 4 , wherein the recognizing comprises updating the loading state of the parcels in the tipper by combining the extracted relative distance of the parcels in the tipper with a recognition result of the object recognition model. 6 . The insertion automation method of claim 1 , further comprising adjusting a gradient angle of an insertion line belt that is adjacent to the tipper based on the recognized loading state of the parcels in the tipper. 7 . The insertion automation method of claim 6 , wherein a damping system is applied to a lower part of the insertion line belt in which the gradient angle is adjusted. 8 . An insertion automation device comprising: one or more processors; and a memory configured to load or store a program executed by the one or more processors, wherein the program comprises: instructions configured to determine an initial gradient change angular velocity of a tipper based on a total weight of parcels in the tipper, recognize a loading state of the parcels in the tipper by inputting images of the parcels in the tipper to an object recognition model, and redetermine a gradient change angular velocity of the tipper based on the recognized loading state, wherein the loading state of the parcels in the tipper comprises at least one of a position, size, and packing material of the parcels in the tipper. 9 . The insertion automation device of claim 8 , wherein the object recognition model comprises a neural network trained to infer a packing material of each of parcels comprised in a training image in response to the training image that is predetermined being input. 10 . The insertion automation device of claim 8 , wherein the object recognition model comprises a neural network trained to infer a size of parcels based on coordinate information of the parcels comprised in a training image in response to the training image that is predetermined being input. 11 . The insertion automation device of claim 8 , wherein the one or more processors are configured to extract a relative distance of the parcels in the tipper through heterogeneous sensors. 12 . The insertion automation device of claim 11 , wherein the one or more processors are further configured to update and recognize the loading state of the parcels in the tipper by combining the extracted relative distance of the parcels in the tipper with a recognition result of the object recognition model. 13 . The insertion automation device of claim 8 , wherein the one or more processors are further configured to adjust a gradient angle of an insertion line belt that is adjacent to the tipper based on the recognized loading state of the parcels in the tipper. 14 . The insertion automation device of claim 13 , wherein a damping system is applied to a lower part of the insertion line belt in which the gradient angle is adjusted.
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using neural networks · CPC title
Control devices, e.g. for safety, warning or fault-correcting · CPC title
Analysis of geometric attributes · CPC title
Camera · CPC title
Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title
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- What does patent US12494061B2 cover?
- Disclosed are an insertion automation method and system based on deep-learning parcel recognition. The insertion automation method includes determining an initial gradient change angular velocity of a tipper based on a total weight of parcels in the tipper, recognizing a loading state of the parcels in the tipper by inputting images of the parcels in the tipper to an object recognition model, a…
- Who is the assignee on this patent?
- Electronics & Telecommunications Res Inst
- What technology area does this patent fall under?
- Primary CPC classification G06V20/50. Mapped technology areas include Physics.
- When was this patent published?
- Publication date Tue Dec 09 2025 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
- What related patents are in patentsdb?
- We list 3 related publications on this page (citations in our corpus or others sharing the same primary CPC).