JP2021157550A5 - - Google Patents
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- JP2021157550A5 JP2021157550A5 JP2020057704A JP2020057704A JP2021157550A5 JP 2021157550 A5 JP2021157550 A5 JP 2021157550A5 JP 2020057704 A JP2020057704 A JP 2020057704A JP 2020057704 A JP2020057704 A JP 2020057704A JP 2021157550 A5 JP2021157550 A5 JP 2021157550A5
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- 238000001514 detection method Methods 0.000 claims 11
- 238000013528 artificial neural network Methods 0.000 claims 4
- 238000011156 evaluation Methods 0.000 claims 1
- 230000006870 function Effects 0.000 claims 1
- 238000000034 method Methods 0.000 claims 1
Claims (9)
前記撮影画像から地色と異なる色を抽出することで色差分画像を作成する第１の作成手段と、
任意の方法により、前記撮影画像中の物体を検知した判定結果と、前記色差分画像とを用いて、前記判定結果で検出されなかった未検出物体を特定する特定手段と、
を有することを特徴とする検出装置。 an input means for inputting a photographed image obtained by photographing one or more objects to be detected;
a first creation means for creating a color difference image by extracting a color different from the background color from the captured image;
identifying means for identifying an undetected object that was not detected by the determination result, using the determination result of detecting the object in the captured image and the color difference image by an arbitrary method ;
A detection device comprising :
を有することを特徴とする請求項１に記載の検出装置。 The detection device according to claim 1, characterized by comprising:
を有することを特徴とする請求項２に記載の検出装置。 second determination means for determining the type of object included in the undetected object image using model parameters of the second neural network that has been trained;
3. The detection device according to claim 2 , characterized by comprising:
を有することを特徴とする請求項３に記載の検出装置。 A first determination means for detecting an object included in the captured image using model parameters of a trained first neural network and determining the type of the detected object;
4. The detection device according to claim 3 , characterized by comprising:
検出した物体を、第１の種類を示すクラス又は第２の種類を示すクラスのいずれかに分類することで、前記物体の種類を判定する、ことを特徴とする請求項４に記載の検出装置。 The first determination means is
5. The detection device according to claim 4 , wherein the type of the detected object is determined by classifying the detected object into either a class indicating the first type or a class indicating the second type. .
前記未検出物体画像中に含まれる物体を、第１の種類を示すクラス、第２の種類を示すクラス又は前記検出対象以外を示すクラスのいずれかに分類することで、前記物体が検出対象であるか否かと前記物体が検出対象である場合における種類とを判定する、ことを特徴とする請求項３乃至６の何れか一項に記載の検出装置。 The second determination means is
By classifying an object included in the undetected object image into one of a class indicating a first type, a class indicating a second type, or a class indicating other than the detection target, the object is a detection target. 7. The detection device according to claim 3 , further comprising determining whether or not there is an object and the type of the object when the object is the object to be detected.
前記学習用画像を用いて、前記第１のニューラルネットワークのモデルパラメータと前記第２のニューラルネットワークのモデルパラメータとを学習する学習手段と、を有し、
前記第３の作成手段は、
所定の評価基準に基づいて、前記撮影画像に対して第１の種類を示すラベル又は第２の種類を示すラベルのいずれかを付与する、又は、前記撮影画像中に含まれる物体の画像領域を指定し、該画像領域に対して第一の種類を示すラベル又は第二の種類を示すラベルを付与するアノテーションを行うことで、前記学習用画像を作成する、ことを特徴とする請求項４に記載の検出装置。 a third creating means for creating a learning image from the captured image;
learning means for learning the model parameters of the first neural network and the model parameters of the second neural network using the learning image;
The third creation means is
Based on a predetermined evaluation criterion, the photographed image is given either a label indicating the first type or a label indicating the second type, or an image area of an object included in the photographed image is determined. 5. The learning image is created by specifying and annotating the image area with a label indicating the first type or a label indicating the second type. The detection device described.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2020057704A JP7469100B2 (en) | 2020-03-27 | 2020-03-27 | Detection device and program |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2020057704A JP7469100B2 (en) | 2020-03-27 | 2020-03-27 | Detection device and program |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| JP2021157550A JP2021157550A (en) | 2021-10-07 |
| JP2021157550A5 true JP2021157550A5 (en) | 2022-11-14 |
| JP7469100B2 JP7469100B2 (en) | 2024-04-16 |
Family
ID=77918031
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP2020057704A Active JP7469100B2 (en) | 2020-03-27 | 2020-03-27 | Detection device and program |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP7469100B2 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7360660B1 (en) | 2022-09-09 | 2023-10-13 | 株式会社マーケットヴィジョン | information processing system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4533836B2 (en) * | 2005-12-01 | 2010-09-01 | 株式会社東芝 | Fluctuating region detection apparatus and method |
| JP2007287093A (en) * | 2006-04-20 | 2007-11-01 | Fujitsu Frontech Ltd | Program, method and device for detecting mobile object |
| JP2019008519A (en) * | 2017-06-23 | 2019-01-17 | パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカＰａｎａｓｏｎｉｃ Ｉｎｔｅｌｌｅｃｔｕａｌ Ｐｒｏｐｅｒｔｙ Ｃｏｒｐｏｒａｔｉｏｎ ｏｆ Ａｍｅｒｉｃａ | Mobile body detection method, mobile body learning method, mobile body detector, mobile body learning device, mobile body detection system, and program |
-
2020
- 2020-03-27 JP JP2020057704A patent/JP7469100B2/en active Active
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