JPH04147002A - Inspecting apparatus of surface of fruit and vegetable - Google Patents
Inspecting apparatus of surface of fruit and vegetableInfo
- Publication number
- JPH04147002A JPH04147002A JP27074390A JP27074390A JPH04147002A JP H04147002 A JPH04147002 A JP H04147002A JP 27074390 A JP27074390 A JP 27074390A JP 27074390 A JP27074390 A JP 27074390A JP H04147002 A JPH04147002 A JP H04147002A
- Authority
- JP
- Japan
- Prior art keywords
- fruit
- lightness
- image
- vegetable
- contour
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 235000012055 fruits and vegetables Nutrition 0.000 title claims abstract description 30
- 235000013399 edible fruits Nutrition 0.000 claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 6
- 235000013311 vegetables Nutrition 0.000 claims description 9
- 238000003384 imaging method Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims description 2
- 238000012795 verification Methods 0.000 claims 1
- 238000007689 inspection Methods 0.000 abstract description 3
- 238000000034 method Methods 0.000 abstract description 2
- 238000012545 processing Methods 0.000 description 10
- 241001672694 Citrus reticulata Species 0.000 description 3
- 244000241257 Cucumis melo Species 0.000 description 3
- 235000015510 Cucumis melo subsp melo Nutrition 0.000 description 3
- FJJCIZWZNKZHII-UHFFFAOYSA-N [4,6-bis(cyanoamino)-1,3,5-triazin-2-yl]cyanamide Chemical compound N#CNC1=NC(NC#N)=NC(NC#N)=N1 FJJCIZWZNKZHII-UHFFFAOYSA-N 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241000723267 Diospyros Species 0.000 description 1
- 235000011511 Diospyros Nutrition 0.000 description 1
- 235000007688 Lycopersicon esculentum Nutrition 0.000 description 1
- 244000141359 Malus pumila Species 0.000 description 1
- 241000220324 Pyrus Species 0.000 description 1
- 240000003768 Solanum lycopersicum Species 0.000 description 1
- 235000021016 apples Nutrition 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 235000021017 pears Nutrition 0.000 description 1
Abstract
Description
【発明の詳細な説明】
（産業上の利用分野）
本発明は、みかん、リンゴ、ナシ、トマト、柿などの青
果物の等級を検定する際に、青果物の表面の凹凸状態を
検定する装置に関する。DETAILED DESCRIPTION OF THE INVENTION (Industrial Application Field) The present invention relates to an apparatus for examining the unevenness of the surface of fruits and vegetables, such as mandarin oranges, apples, pears, tomatoes, persimmons, etc., when evaluating the grade of the fruits and vegetables.
（従来の技術）
従来、青果物を等絞付けする装置の一例としては、青果
物の上方に設置したカメラで青果物を撮影し、その撮影
によって得られる画像に基づいて傷の有無や色、形状を
判定し、これによって等級を検定するものか知られてい
る。(Prior Art) Conventionally, as an example of a device for evenly squeezing fruits and vegetables, a camera installed above the fruits and vegetables photographs the fruits and vegetables, and based on the images obtained, the presence or absence of scratches, color, and shape are determined. However, it is known whether this is used to test the grade.
（発明か解決しようとする課題）
ところが、従来装置では、例えばみかんのうき皮の状態
のように、青果物の表面における凹凸状態を知ることか
できないので、青果物の等級検定の際にその表面の凹凸
状態を反映できず、もって青果物の等級検定の精度か低
いという問題が生じていた。(Problem to be solved by the invention) However, with conventional devices, it is not possible to know the unevenness of the surface of fruits and vegetables, such as the condition of the peel of mandarin oranges. The problem was that the accuracy of the fruit and vegetable grading test was low because the condition could not be reflected.
そこで、本発明は、以上の点に鑑み、上記の問題を解消
することを目的とする。Therefore, in view of the above points, the present invention aims to solve the above problems.
（課題を解決するための手段）
かかる目的を達成するために、本発明は以下のように構
成した。(Means for Solving the Problems) In order to achieve the above object, the present invention was constructed as follows.
すなわち、本発明は、青果物を照射する光源と、前記青
果物を撮像する撮像手段と、その撮像した画像からあら
かしめ数段階に設定した各明度の部分をそれぞれ抽出す
る抽出手段と、その抽出した各明度の部分をそれぞれ結
んで明度等高線のパターンを作成する明度等高線パター
ン作成手段と、その作成した明度等高線のパターンに基
づいて青果物の表面における凹凸状態を検出する凹凸状
態検出手段と、を備えてなる。That is, the present invention provides a light source for illuminating fruits and vegetables, an imaging means for taking an image of the fruits and vegetables, an extraction means for extracting parts of each brightness set in several stages from the taken image, and a light source for each of the extracted fruits and vegetables. A lightness contour pattern creating means for creating a lightness contour pattern by connecting the lightness portions, and an uneven state detection means for detecting an uneven state on the surface of the fruit or vegetable based on the created lightness contour pattern. .
（作用）
このように構成する本発明では、抽出手段は、撮像手段
が撮像した画像からあらかじめ数段階に設定した各明度
の部分をそれぞれ抽出する。(Function) In the present invention configured as described above, the extraction means extracts portions of each brightness set in advance in several levels from the image taken by the imaging means.
明度等高線パターン作成手段は、その抽出した各明度の
部分をそれぞれ結んで明度等高線のパターンを作成する
。The lightness contour pattern creation means connects the extracted lightness portions to create a lightness contour pattern.
凹凸状態検出手段は、その作成された明度等高線のパタ
ーンの形態から青果物の表面における凹凸状態を検出す
る。例えば、明度等高線の形状やその間隔から青果物の
表面における凹凸状態がわかるので、それらを調べるこ
とによりその凹凸状態を検出できる。The uneven state detecting means detects the uneven state on the surface of the fruit or vegetable from the form of the created brightness contour line pattern. For example, since the shape of the brightness contour lines and their intervals can be used to determine the unevenness on the surface of fruits and vegetables, the unevenness can be detected by examining them.
（実施例）
以下、図面を参照して本発明の実施例について説明する
。(Example) Hereinafter, an example of the present invention will be described with reference to the drawings.
図において、１は青果物ａを収容するパケット２を有す
るパケットコンベアであり、その側部にパケット２の通
過を検出するパケット検出センサ３を設ける。In the figure, 1 is a packet conveyor having packets 2 containing fruits and vegetables a, and a packet detection sensor 3 for detecting the passage of the packets 2 is provided on the side thereof.
パケットコンベア１の左右の上方には、パケットコンベ
ア１で搬送されてくる青果物ａを照射する光源４，４を
それぞれ設ける。またパケットコンベア１の上方には、
搬送されてくる青果物ａを撮像する撮像手段としてテレ
ビカメラやＣＯＤカメラなどのカメラ５を配置する。Above the left and right sides of the packet conveyor 1, light sources 4, 4 are provided, respectively, for illuminating the fruits and vegetables a conveyed by the packet conveyor 1. Also, above the packet conveyor 1,
A camera 5 such as a television camera or a COD camera is arranged as an imaging means for taking an image of the fruits and vegetables a being conveyed.
パケット検出センサ３、およびカメラ５は、画像処理用
コンピュータ６の入力側に接続する。画像処理用コンピ
ュータ６は、後述のようにカメラ５が撮像した青果物ａ
の画像を処理する。The packet detection sensor 3 and camera 5 are connected to the input side of an image processing computer 6. The image processing computer 6 stores fruits and vegetables a captured by the camera 5 as described later.
Process images.
画像処理用コンピュータ６の出力側には、表示装置やプ
リンタなどからなる画像出力装置７を接続する。An image output device 7 consisting of a display device, a printer, etc. is connected to the output side of the image processing computer 6.
次に、このように構成する実施例の画像処理例を第２図
を参照して説明する。Next, an example of image processing in the embodiment configured as described above will be explained with reference to FIG.
いま、パケット検出センサ３が青果物ａを収容したパケ
ット２か検査位置を通過したことを検出すると、カメラ
５で撮像した青果物ａの画像を入力する（ステップＳＬ
、Ｓ２）。Now, when the packet detection sensor 3 detects that the packet 2 containing the fruits and vegetables a has passed the inspection position, the image of the fruits and vegetables a captured by the camera 5 is input (step SL).
, S2).
そして、そのカラー人力画像には、色を特定する色相、
彩度、明度の各情報が含まれているので、この各情報を
用いて青果物ａの色評価を行う（ステップＳ３）、、す
なわち、この色評価は、カラー人力画像から所定の基準
色を中心に設定した色差の範囲にある色の画像（画素）
を抽出し、その抽出した画像の面積を求めることにより
行う。このように抽出した画像の一例は、第３図の斜線
で示すようになる。Then, in that color human image, the hue that specifies the color,
Since each information of saturation and brightness is included, this information is used to evaluate the color of fruit or vegetable a (step S3).In other words, this color evaluation is performed by using a color manual image based on a predetermined reference color. An image (pixels) of colors within the color difference range set to
This is done by extracting the image and calculating the area of the extracted image. An example of the image extracted in this way is shown by diagonal lines in FIG.
次に、入力画像からあらかじめ数段階に設定した各明度
（輝度）の画像部分をそれぞれ抽出し、その抽出した各
明度の画像部分をそれぞれ結んで明度等高線のパターン
を作成する（ステップＳ４）。この明度等高線のパター
ンの一例を示すと、第４図となる。Next, image portions of each brightness (brightness) set in several levels in advance are extracted from the input image, and a brightness contour pattern is created by connecting the extracted image portions of each brightness (step S4). An example of the brightness contour pattern is shown in FIG. 4.
次に、この明度等高線のパターンに基つき、青果物ａの
中心から各明度等高線間の間隔を算出する（ステップＳ
５）。この間隔の算出方向は、例えば第５図で示す矢印
の各方向とする。Next, based on this pattern of brightness contour lines, the interval between each brightness contour line from the center of fruit or vegetable a is calculated (step S
5). The directions in which this interval is calculated are, for example, the directions of the arrows shown in FIG.
ところで、明度等高線の間隔か狭くなっているのは、そ
の部分が凹または凸の状態であることを示す。そこで、
これを利用してステップＳ５で算出した各間隔に基づき
、青果物ａの表面における凹凸状態を評価する（ステッ
プＳ６）。例えば青果物ａがみかん等のときには、その
凹凸によりうき皮の状態を観察評価できる。By the way, narrow intervals between brightness contour lines indicate that the area is concave or convex. Therefore,
Utilizing this, the uneven state on the surface of the fruit or vegetable a is evaluated based on each interval calculated in step S5 (step S6). For example, when the fruit a is a mandarin orange, the condition of the peel can be observed and evaluated based on its unevenness.
次に、上記の入力画像から背景の色を白または黒に変換
すると残余の画像が青果物ａを表すので、それにより青
果物ａの形状およびその面積をそれぞれ求める（ステッ
プＳ７．Ｓ８）。Next, when the background color is converted from the above input image to white or black, the remaining image represents the fruit or vegetable a, and the shape and area of the fruit or vegetable a are determined accordingly (steps S7 and S8).
このように、本実施例では、青果物の色、形状、大きさ
の各評価をすることに加え、青果物の表面における凹凸
状態を評価できるようにしたので、青果物の等級検定の
際にその表面の凹凸状態を反映でき、もって青果物の等
級検定の精度が向上する。In this way, in this example, in addition to evaluating the color, shape, and size of fruits and vegetables, it is also possible to evaluate the unevenness on the surface of fruits and vegetables. The unevenness can be reflected, which improves the accuracy of fruit and vegetable grading.
次に、第１図で示した装置により、メロンの線部分を抽
出する画像処理例について、第６図および第７図を参照
して説明する。Next, an example of image processing for extracting line parts of a melon using the apparatus shown in FIG. 1 will be described with reference to FIGS. 6 and 7.
この場合には、まず最低輝度差ｅを設定する（ステップ
５１１）。そして、第７図に示すように輝度測定ライン
を設定する（ステップ５１２）。In this case, the minimum brightness difference e is first set (step 511). Then, a brightness measurement line is set as shown in FIG. 7 (step 512).
次に、その設定した輝度測定ライン上の輝度分布から、
その輝度の極大値Ｘおよび極小値ｙをそれぞれ求める（
ステップ５１３）。Next, from the brightness distribution on the set brightness measurement line,
Find the local maximum value X and local minimum value y of the brightness, respectively (
Step 513).
次に、その求めた極大値Ｘと極小値ｙとの差（ｘ−ｙ）
を求め、その差がステップＳｌｌで設定した最低輝度差
ｅを上回るときには、その極大値Ｘの位置、すなわち第
７図で示す縞の位置Ａをメモリに格納する（ステップＳ
１４．１５）。Next, the difference (x-y) between the obtained local maximum value X and local minimum value y
is calculated, and when the difference exceeds the minimum luminance difference e set in step Sll, the position of the maximum value X, that is, the position A of the stripe shown in FIG. 7, is stored in the memory (step S
14.15).
このような画像処理によれば、メロンの縞の部分を高精
度に検出できる。According to such image processing, the striped portion of the melon can be detected with high precision.
（発明の効果）
以上のように本発明では、撮像手段で撮像した画像から
あらかじめ数段階に設定した各明度の部分をそれぞれ抽
出し、その抽出した各明度の部分をそれぞれ結んで明度
等高線のパターンを作成し、その作成した明度等高線パ
ターンに基づいて青果物の表面における凹凸状態を検出
するようにした。(Effects of the Invention) As described above, in the present invention, parts of each brightness set in several levels in advance are extracted from an image captured by an imaging means, and the extracted parts of each brightness are connected to form a brightness contour line pattern. was created, and the uneven state on the surface of fruits and vegetables was detected based on the created brightness contour pattern.
従って、本発明は、従来の青果物の等級検定装置に組み
込む等により使用すれば、その等級検定の際にその表面
の凹凸状態を知って等級検定に反映でき、もって青果物
の等級検定の精度が向上する。Therefore, if the present invention is incorporated into a conventional fruit and vegetable grading device, it is possible to know the unevenness of the surface and reflect it in the grading, thereby improving the accuracy of the grading of fruits and vegetables. do.
第１図は本発明実施例の全体構成を示す図、第２図は本
発明実施例の画像処理例を示すフローチャート、第３図
〜第５図はそれぞれ画像処理例を説明する図、第６図は
メロンの縞を抽出する画像処理例を示すフローチャート
、第７図はそれを説明する図である。
１はパケットコンベア、４は光源、５はカメラ、６は画
像処理用コンピュータである。
第
図
第 ２ 図
弔
図
第
ア
図FIG. 1 is a diagram showing the overall configuration of an embodiment of the present invention, FIG. 2 is a flowchart showing an example of image processing in the embodiment of the present invention, FIGS. 3 to 5 are diagrams each explaining an example of image processing, and FIG. The figure is a flowchart showing an example of image processing for extracting melon stripes, and FIG. 7 is a diagram explaining it. 1 is a packet conveyor, 4 is a light source, 5 is a camera, and 6 is an image processing computer. Figure 2 Figure 2 Funeral map Figure A
Claims (1)
度の部分をそれぞれ抽出する抽出手段と、その抽出した
各明度の部分をそれぞれ結んで明度等高線のパターンを
作成する明度等高線パターン作成手段と、 その作成した明度等高線のパターンに基づいて青果物の
表面における凹凸状態を検出する凹凸状態検出手段と、 を備えてなる青果物の表面検定装置。[Scope of Claims] A light source that irradiates fruits and vegetables, an imaging device that images the fruits and vegetables, an extraction device that extracts portions of each brightness set in advance in several levels from the captured image, and each of the extracted brightnesses. A fruit or vegetable product comprising: a lightness contour pattern creation means for creating a pattern of lightness contour lines by connecting each of the parts; and an uneven state detection means for detecting an uneven state on the surface of the fruit or vegetable based on the created lightness contour pattern. surface verification device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP27074390A JPH04147002A (en) | 1990-10-09 | 1990-10-09 | Inspecting apparatus of surface of fruit and vegetable |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP27074390A JPH04147002A (en) | 1990-10-09 | 1990-10-09 | Inspecting apparatus of surface of fruit and vegetable |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH04147002A true JPH04147002A (en) | 1992-05-20 |
Family
ID=17490358
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP27074390A Pending JPH04147002A (en) | 1990-10-09 | 1990-10-09 | Inspecting apparatus of surface of fruit and vegetable |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH04147002A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6162392A (en) * | 1997-08-01 | 2000-12-19 | The Coca-Cola Company | Method and apparatus for super critical treatment of liquids |
| WO2006129391A1 (en) * | 2005-06-03 | 2006-12-07 | Mayekawa Mfg.Co., Ltd. | Apparatus for detecting contaminants in food |
| JP2014163865A (en) * | 2013-02-27 | 2014-09-08 | Iwate Industrial Research Center | Sake rice inspection apparatus |
-
1990
- 1990-10-09 JP JP27074390A patent/JPH04147002A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6162392A (en) * | 1997-08-01 | 2000-12-19 | The Coca-Cola Company | Method and apparatus for super critical treatment of liquids |
| WO2006129391A1 (en) * | 2005-06-03 | 2006-12-07 | Mayekawa Mfg.Co., Ltd. | Apparatus for detecting contaminants in food |
| JP2014163865A (en) * | 2013-02-27 | 2014-09-08 | Iwate Industrial Research Center | Sake rice inspection apparatus |
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