JP2003156430A - Method and device for detecting water, ice and snow on road surface - Google Patents
Method and device for detecting water, ice and snow on road surfaceInfo
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- JP2003156430A JP2003156430A JP2001395440A JP2001395440A JP2003156430A JP 2003156430 A JP2003156430 A JP 2003156430A JP 2001395440 A JP2001395440 A JP 2001395440A JP 2001395440 A JP2001395440 A JP 2001395440A JP 2003156430 A JP2003156430 A JP 2003156430A
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- ice
- snow
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Abstract
Description
【０００１】[0001]
【産業上の利用分野】この発明は冬期間の道路やロード
ヒーティングの路面上の水、氷、雪をそれぞれ検知し、
凍結監視や融雪剤散布時期を判断するセンサ及びロード
ヒーティング制御用のセンサとして利用するための路面
上に分布する水と氷と雪の検知方法に関する。更に詳し
く言えば、この発明は、水・氷分離フィルタ、雪分離フ
ィルタ及び水分分離フィルタのそれぞれのフィルタを通
してカメラにより路面をとらえ撮像した３枚の画像から
得ることができる雪画像、水分画像、水画像、氷画像に
基づき、まず雪画像から雪の検知を行い、雪画像と水分
画像との排他的論理和演算により得ることができる水氷
演算画像と水画像の論理積から水の検知を行い、水氷演
算画像と氷画像の論理積から氷の検知を行うことによる
路面上の水と氷と雪の検知方法に関する。BACKGROUND OF THE INVENTION This invention detects water, ice, and snow on roads and road heating surfaces in winter,
The present invention relates to a method for detecting water, ice, and snow distributed on a road surface for use as a sensor for determining freezing monitoring and snow melting agent spraying time, and a sensor for road heating control. More specifically, the present invention provides a snow image, a moisture image, and a water image that can be obtained from three images captured by capturing the road surface with a camera through a water / ice separation filter, a snow separation filter, and a water separation filter. First, snow is detected from the snow image based on the image and the ice image, and water is detected from the logical product of the water ice calculation image and the water image, which can be obtained by the exclusive OR operation of the snow image and the water image. The present invention relates to a method of detecting water, ice, and snow on a road surface by detecting ice from a logical product of a water ice calculation image and an ice image.
【０００２】[0002]
【従来の技術】路面上の水と氷と雪の検知方式として、
すでに実用化になっている代表的なセンサとして、電極
式路面水分センサがある。このセンサは路面上の８０ｃ
ｍ２程度の小領域の水分しかとらえることができない。
また、水と氷・雪の識別は可能であるが、その識別精度
は十分ではなく、氷と雪の識別はできない。したがっ
て、路面上に水、氷、雪が混在していても、それぞれを
検知しての位置と広さの分布状態の把握は不可能であ
る。さらに、路面埋込型であることから、摩耗・劣化等
による誤動作が生じる。これら検知精度と信頼性に問題
があるため、ロードヒーティングの正確な制御ができ
ず、路面の凍結を招いたり過剰なヒーティングを行って
いるのが現状である。2. Description of the Related Art As a method of detecting water, ice and snow on the road surface,
An electrode type road surface moisture sensor is a typical sensor that has already been put to practical use. This sensor is 80c on the road
Only water in a small area of about m 2 can be captured.
Further, although water and ice / snow can be discriminated from each other, the discrimination accuracy is not sufficient and ice and snow cannot be discriminated from each other. Therefore, even if water, ice, and snow are mixed on the road surface, it is impossible to detect the respective positions and grasp the distribution state of the position and the area. Furthermore, since it is a road surface embedded type, malfunction occurs due to wear and deterioration. Due to these problems in detection accuracy and reliability, it is not possible to accurately control road heating, which leads to freezing of the road surface and excessive heating.
【０００３】他に、一般的ではないが、路面の水と氷と
雪の検知として、すでに実用化になっている方式は、投
光した光の拡散光と反射光をとらえて、それらの強度の
程度から検知する方式があり、この方式によるセンサが
小糸工業株式会社から市販されている。しかし、カメラ
のような２次元の広領域の検知ではなく、基本的にはフ
ォトダイオードを用いた受光による小領域の検知であ
り、例えばロードヒーティングの領域をすべて検知する
には、この様な方式のセンサを複数個設置することが必
要になり、経済的ではなく、また設置個所も限られ、全
領域を検知することは難しい。In addition, although not general, a method that has already been put into practical use for detecting water, ice, and snow on the road surface captures the diffused light and the reflected light of the projected light and detects their intensity. There is a method of detecting from this degree, and a sensor based on this method is commercially available from Koito Industry Co., Ltd. However, it is not a two-dimensional wide area detection like a camera but basically a small area detection by light reception using a photodiode. For example, in order to detect the entire area of load heating, It is necessary to install a plurality of sensors of the method, which is not economical, and the installation location is limited, so it is difficult to detect the entire area.
【０００４】なお、この他に、ＣＣＤカメラからの色信
号比（Ｒ、Ｇ、Ｂ信号比）により検知する方式、路面の
形状特徴量を利用するパターンマッチングによる検知方
式（１９９５電子情報通信学会ソサイエティ大会，ｐ
ｐ．３１６−３１７，１９９５．）、ＣＣＤカメラから
得られる輝度信号を用いて、路面の光沢度から検知する
方式（電子情報通信学会論文誌，ｖｏｌ．Ｊ８１−Ｄ−
ＩＩ，Ｎｏ．１０，ｐｐ．２３０１−２３１０，Ｏｃ
ｔ．１９９８．）、水平と垂直方向の偏向フィルタを付
けたレンズを通してカメラによりとらえ、その垂直偏向
成分と水平偏向成分の差から検知する方式（照明学会
誌，ｖｏｌ．６６，ｎｏ．１０，ｐｐ．４５０−４５
４，１９８２．）がある。何れも、検知精度に問題があ
ったり、複数台のカメラが必要になりシステムが複雑化
するなどの問題を内在しているとともに、水と氷と雪を
それぞれ正確に検知することはできない。In addition to the above, a detection method based on a color signal ratio (R, G, B signal ratio) from a CCD camera and a detection method based on pattern matching using a road surface shape feature amount (1995 Institute of Electronics, Information and Communication Society Competition, p
p. 316-317, 1995. ), A method of detecting from the glossiness of the road surface using a luminance signal obtained from a CCD camera (Journal of the Institute of Electronics, Information and Communication Engineers, vol. J81-D-
II, No. 10, pp. 2301-2310, Oc
t. 1998. ), And a method of detecting the difference from the vertical deflection component and the horizontal deflection component, which is detected by a camera through a lens having horizontal and vertical deflection filters (Illumination Society of Japan, vol. 66, no. 10, pp. 450-45).
4, 1982. ). All of these have inherent problems such as a problem in detection accuracy and the need for multiple cameras to complicate the system, and cannot detect water, ice and snow accurately.
【０００５】また、発光器を正反射及び乱反射が生じる
ようにそれぞれ２台を設置し、その正反射光と乱反射光
を１台の受光器で受光することによる路面水分計測装置
が公開特許（特開平８−３１３４３５）にあるが、計測
領域の大きさには限界があり、２次元の広領域の検知は
難しく、水と氷と雪を検知しての各分布状態の把握は不
可能である。Further, a road surface water content measuring device is provided in which two light emitters are installed so that specular reflection and diffuse reflection occur, and the specular reflection light and diffuse reflection light are received by one light receiver. Kaihei 8-313435), but the size of the measurement area is limited, and it is difficult to detect a two-dimensional wide area, and it is impossible to grasp each distribution state by detecting water, ice, and snow. .
【０００６】[0006]
【発明が解決しようとする問題点】水分の分光特性をみ
ると比較的大きな吸収帯が赤外波長域に存在する。この
吸収帯を利用しての赤外センサや赤外カメラによる路面
上の水分検知が可能である。しかし、水、氷、雪のそれ
ぞれについて吸収の大きな赤外波長を中心波長に持つ３
つのバンドパスフィルタを通して、これら３つの波長の
光を含む赤外投光器の光の下のみで、これら３つの波長
に感度を持つ赤外カメラによりとらえて得た３枚のディ
ジタル画像からの２値化処理により、水、氷、雪をそれ
ぞれ検知するには、背景になる乾燥路面についての吸光
の度合いと水、氷、雪のそれぞれの吸光の度合いのとの
差の大きさが重要になる。一般に、路面水分検知では、
背景となる乾燥路面は黒色系のアスファルト路面である
ため、光の吸光の度合いは大きく、特に、その吸光の度
合いは水または氷の検知に用いる赤外波長の光の吸光の
度合いと似ている部分があることから、水または氷の乾
燥路面からの分離・検知は難しい。例えば、検知に用い
た赤外波長の光について、乾燥路面の吸光の度合いと水
の吸光の度合いが似ているため、水のみの乾燥路面から
の分離・検知は難しく、水と背景である乾燥路面が混在
した検知結果になる。これは氷や雪についても同様のこ
とが起こり得る。Problems to be Solved by the Invention Looking at the spectral characteristics of water, a relatively large absorption band exists in the infrared wavelength range. Using this absorption band, it is possible to detect moisture on the road surface with an infrared sensor or infrared camera. However, it has an infrared wavelength with large absorption for each of water, ice, and snow.
Binarization from three digital images obtained through an infrared camera sensitive to these three wavelengths only under the light of an infrared projector containing these three wavelengths of light through one bandpass filter. In order to detect water, ice, and snow by processing, the magnitude of the difference between the degree of light absorption on the background dry road surface and the degree of light absorption on water, ice, and snow is important. Generally, in road surface moisture detection,
Since the dry road surface that is the background is a blackish asphalt road surface, the degree of light absorption is large, and in particular, the degree of light absorption is similar to the degree of light absorption of the infrared wavelength used to detect water or ice. Since there are parts, it is difficult to separate and detect water or ice from a dry road surface. For example, regarding the light of the infrared wavelength used for detection, since the degree of absorption of water on the dry road surface is similar to the degree of absorption of water on the dry road surface, it is difficult to separate and detect only water from the dry road surface. The detection result is a mixture of road surfaces. The same can happen for ice and snow.
【０００７】したがって、水、氷、雪のそれぞれについ
て吸収の度合いが大きな赤外波長を中心波長に持つ３つ
のバンドパスフィルタのみを用いて、水、氷、雪の各領
域を検知することは難しい。Therefore, it is difficult to detect each region of water, ice, and snow by using only three bandpass filters each having an infrared wavelength having a large degree of absorption of water, ice, and snow as a central wavelength. .
【０００８】また、バンドパスフィルタを通しての撮像
の際に、各フィルタを切り換えて撮像する場合には、赤
外投光器以外の光の影響を受けると、各フィルタにより
撮像した画像はその明るさの変動を受けるため、水、
氷、雪をそれぞれ分離するために設定したしきい値での
２値化処理による水、氷、雪の検知ができない。このた
め乾燥路面のみならず、水、氷、雪もそれぞれ混在する
検知結果になるため、さらにそれぞれの分離・検知は難
しくなる。これは日射の変動への対策が必要であること
を意味する。Further, in the case of taking an image through a bandpass filter, when switching the respective filters to take an image, when the influence of light other than the infrared projector is exerted, the image taken by each filter changes its brightness. To receive water,
Water, ice, and snow cannot be detected by the binarization process with the threshold values set for separating ice and snow. For this reason, not only the dry road surface but also water, ice, and snow are mixed, so that it becomes more difficult to separate and detect each. This means that measures against fluctuations in solar radiation are necessary.
【０００９】[0009]
【問題点を解決するための手段】水のみ、氷のみの検知
を直接行おうとした場合には、乾燥路面も一部混在した
検知結果になる。そこで、まず雪についてある特定の中
心波長のバンドパスフィルタを通して撮像した画像に基
づき２値化処理することで、水のみや氷のみの検知に比
べて、雪がより精度よく検知できることに着目し、この
より精度よく検知できる雪の領域を先に求める。次に、
水と氷と雪の一括したものがより精度よく検知できるこ
とに着目し、このより精度よく検知できる水と氷と雪の
領域を先に求める。次に、この水と氷と雪の領域と求め
た雪の領域を比較することで、より精度よく水と氷のみ
の領域が求められる。最後に、水または氷についての吸
光の度合いが大きい波長を中心波長に持つバンドパスフ
ィルタを通して撮像した画像に基づき、水のみ、または
氷のみを求めた結果を先に求めた水と氷の領域によって
検証することにより、日射の変動があってもより精度よ
く水、氷、雪のそれぞれの検知を行う。[Means for Solving Problems] When it is attempted to directly detect only water or only ice, the result of detection is that a part of the dry road surface is mixed. Therefore, first, focusing on the fact that by performing binarization processing based on an image taken of a snow through a bandpass filter having a certain center wavelength, the snow can be detected more accurately than the detection of only water or only ice. The area of snow that can be detected with higher accuracy is obtained first. next,
Focusing on the fact that a batch of water, ice, and snow can be detected more accurately, we first seek the area of water, ice, and snow that can be detected more accurately. Next, by comparing the obtained snow region with the water, ice, and snow regions, the region of only water and ice can be obtained more accurately. Finally, based on the image taken through a bandpass filter having a wavelength at which the degree of absorption of water or ice is large as the central wavelength, the result of obtaining only water or only ice is determined by the area of water and ice previously obtained. By verifying, water, ice, and snow will be detected more accurately even if there is a change in solar radiation.
【００１０】以上が本発明の特徴である。The above are the features of the present invention.
【００１１】本発明である特許請求の範囲の請求項１に
よる水、氷、雪の各領域の分離・検知を説明する。Separation / detection of water, ice, and snow regions according to claim 1 of the present invention will be described.
【００１２】水についての吸光の度合いと氷についての
吸光の度合いが異なる光の波長を中心波長に持つバンド
パスフィルタを通して撮像した画像に基づき、水のみの
検知を行い、その結果、乾燥路面も混在した検知結果で
ある２値画像Ｐになった場合、水と氷が精度よく検知さ
れた結果である２値画像Ｕがすでに得られていると、Ｐ
とＵとの論理積により乾燥路面を含まない水のみの検知
結果が得られる。Only water is detected based on an image taken through a band-pass filter having a wavelength of light whose central wavelength is different from that of water and that of ice. As a result, a dry road surface is mixed. When a binary image P is obtained as a result of the detection, the binary image U, which is the result of accurate detection of water and ice, has already been obtained.
By the logical product of U and U, the detection result of only water that does not include a dry road surface is obtained.
【００１３】同様に、水についての吸光の度合いと氷に
ついての吸光の度合いが異なる光の波長を中心波長に持
つバンドパスフィルタを通して撮像した画像に基づき、
氷のみの検知を行い、その結果、乾燥路面も混在した検
知結果である２値画像Ｒになった場合、水と氷が精度よ
く検知された結果である２値画像Ｕがすでに得られてい
ると、ＲとＵとの論理積により乾燥路面を含まない氷の
みの検知結果が得られる。Similarly, based on an image taken through a bandpass filter having a central wavelength of light having a different degree of absorption of water and a degree of absorption of ice,
When only the ice is detected, and as a result, the binary image R is the detection result in which the dry road surface is also mixed, the binary image U that is the result of the accurate detection of water and ice has already been obtained. And a logical product of R and U, a detection result of only ice that does not include a dry road surface can be obtained.
【００１４】ここで、水と氷の検知結果である２値画像
Ｕは次のように得ることができる。水と氷と雪について
の吸光の度合いが大きい光の波長を中心波長に持つバン
ドパスフィルタを通して撮像した画像に基づく２値化処
理により、水と氷と雪が乾燥路面から精度よく分離・検
知された結果である２値画像Ｔと、雪についての吸光の
度合いが小さい光の波長を中心波長に持つバンドパスフ
ィルタを通して撮像した画像に基づく２値化処理によ
り、雪のみが精度よく検知された結果である２値画像Ｑ
との排他的論理和により、より精度のよい水と氷につい
ての検知結果である２値画像Ｕが得られる。Here, the binary image U, which is the detection result of water and ice, can be obtained as follows. Water, ice and snow were accurately separated and detected from the dry road surface by the binarization process based on the image taken through the bandpass filter that has the wavelength of light having a large degree of absorption of water, ice and snow as the central wavelength. Only the snow is accurately detected by the binarization processing based on the resulting binary image T and the image captured through the bandpass filter having the wavelength of light having a small light absorption degree for snow as the central wavelength. A certain binary image Q
The binary image U, which is a more accurate detection result of water and ice, is obtained by the exclusive OR of
【００１５】ここで、水と氷と雪についての２値画像Ｔ
と雪についての２値画像Ｑについては乾燥路面は混在せ
ず、精度よく水と氷と雪、雪がそれぞれ検知されてい
る。これは、それぞれ乾燥路面に比べて、水と氷と雪に
ついての吸光の度合いが非常に大きな光の波長を中心波
長とするバンドパスフィルタと、乾燥路面や水、氷に比
べて、雪についての吸光の度合いが非常に小さな光の波
長を中心波長とするバンドパスフィルタを通して撮像さ
れた各画像に基づき２値化処理して得られたものだから
である。Here, a binary image T of water, ice and snow
In the binary image Q of snow and snow, the dry road surface does not coexist, and water, ice, snow, and snow are accurately detected. This is because the bandpass filters whose center wavelengths are the wavelengths of light whose absorption levels for water, ice and snow are very large compared to dry road surfaces, and for snow compared to dry road surfaces, water and ice, respectively. This is because it is obtained by performing binarization processing based on each image taken through a bandpass filter having a wavelength of light having a very small degree of light absorption as a central wavelength.
【００１６】以上の検知手順を図１に示す。The above detection procedure is shown in FIG.
【００１７】図１において、１では雪画像を得る。これ
が雪の検知結果になる。２では水分画像を得る。３では
水氷演算画像を得る。４では水画像を得る。５では氷画
像を得る。６では水画像と水氷演算画像との論理積演算
を行う。この演算結果が水の検知結果になる。７では氷
画像と水氷演算画像との論理積演算を行う。この演算結
果が氷の検知結果になる。In FIG. 1, a snow image is obtained at 1. This is the snow detection result. At 2, a moisture image is obtained. In 3, the water ice calculation image is obtained. In 4, a water image is obtained. At 5, an ice image is obtained. In 6, the logical product operation of the water image and the water ice operation image is performed. This calculation result becomes the water detection result. At 7, the logical product operation of the ice image and the water ice operation image is performed. This calculation result becomes the ice detection result.
【００１８】次に本発明である特許請求の範囲の請求項
２による水、氷、雪の検知装置を説明する。Next, a water, ice and snow detecting device according to claim 2 of the present invention will be described.
【００１９】図１の検知を実現する路面上の水と氷と雪
の検知装置の構成を図２に示す。FIG. 2 shows the configuration of a water / ice / snow detecting device on the road surface for realizing the detection shown in FIG.
【００２０】日射計８により計測された日射量に応じて
リモートアイリス機能付レンズ９の絞りが制御され、雪
分離フィルタ１０、水・氷分離フィルタ１１、水分分離
フィルタ１２の各フィルタを通して撮像される画像は、
すべて日射のない状況での赤外投光器１３、１４の投光
の下の夜間撮像相当の画像になる。The aperture of the lens 9 with the remote iris function is controlled according to the amount of solar radiation measured by the pyranometer 8, and an image is taken through the snow separation filter 10, the water / ice separation filter 11 and the water separation filter 12. The image is
All of the images correspond to nighttime imaging under the projection of the infrared projectors 13 and 14 in a situation where there is no solar radiation.
【００２１】３枚のフィルタは等間隔で、歯車１５にセ
ットされる。この歯車１５はステッピングモータ１６の
軸に固定された歯車１７により回転し、すべてのフィル
タはリモートアイリス機能付レンズ９にほぼ密着するよ
うに位置決めされている。The three filters are set on the gear 15 at equal intervals. The gear 15 is rotated by a gear 17 fixed to the shaft of a stepping motor 16, and all the filters are positioned so as to be in close contact with the lens 9 having the remote iris function.
【００２２】なお、各フィルタごとに撮像画像が夜間撮
像相当画像になるように、計測された日射量に応じてリ
モートアイリス機能付レンズ９の絞りが制御装置１８に
より制御される。この制御のための日射量に応じたリモ
ートアイリス機能付レンズ９の絞り値のデータは各フィ
ルタごとにあらかじめ与えられている。Note that the aperture of the lens 9 with the remote iris function is controlled by the controller 18 in accordance with the measured amount of solar radiation so that the captured image for each filter becomes an image equivalent to nighttime captured image. Data of the aperture value of the lens 9 with a remote iris function according to the amount of solar radiation for this control is given in advance for each filter.
【００２３】まず、ステッピングモータ１６が動作し、
磁気１９とスイッチ２０からなる磁気スイッチにより原
点補正され、雪分離フィルタ１０がリモートアイリス機
能付レンズ９にセッテングされる。赤外投光器１３、１
４が投光され、この雪分離フィルタ１０を通して路面が
撮像され、映像信号は画像入力装置２１に入力され、デ
ジタル画像を得る。次に、ステッピングモータ１６が所
定のパルス分だけ動作し、水・氷分離フィルタ１１がリ
モートアイリス機能付レンズ９にセッテングされる。こ
の水・氷分離フィルタ１１を通して路面が撮像され、映
像信号は画像入力装置２１に入力されデジタル画像を得
る。次に、ステッピングモータが所定のパルス分だけ動
作し、水分分離フィルタ１２がリモートアイリス機能付
レンズ９にセッテングされる。この水分分離フィルタ１
２を通して路面が撮像され、映像信号は画像入力装置２
１に入力されデジタル画像を得る。赤外投光器１３、１
４が消灯される。First, the stepping motor 16 operates,
The origin is corrected by a magnetic switch including a magnet 19 and a switch 20, and the snow separation filter 10 is set on the lens 9 with the remote iris function. Infrared projectors 13, 1
4 is projected, the road surface is imaged through the snow separation filter 10, and the video signal is input to the image input device 21 to obtain a digital image. Next, the stepping motor 16 operates for a predetermined pulse, and the water / ice separation filter 11 is set on the lens 9 with the remote iris function. The road surface is imaged through the water / ice separation filter 11, and the video signal is input to the image input device 21 to obtain a digital image. Next, the stepping motor operates for a predetermined pulse, and the water separation filter 12 is set on the lens 9 with the remote iris function. This water separation filter 1
The image of the road surface is captured through the image input device 2
1 to obtain a digital image. Infrared projectors 13, 1
4 is turned off.
【００２４】得られた各デジタル画像は画像処理装置２
２により２値化され、雪画像、水分画像、水画像と氷画
像が求められる。これら４つの２値画像を図１の検知手
順にしたがって画像処理装置２２により処理することに
より、水、氷、雪の各領域が検知される。The obtained digital images are processed by the image processing device 2.
2 is binarized to obtain a snow image, a water image, a water image and an ice image. By processing these four binary images by the image processing device 22 according to the detection procedure of FIG. 1, the water, ice, and snow regions are detected.
【００２５】なお、赤外カメラ２３、リモートアイリス
機能付レンズ９、フィルタ１０、１１、１２、歯車１
５、歯車１７、ステッピングモータ１６、磁気１９、ス
イッチ２０は、カメラハウジング２４に収納される。カ
メラハウジング２４内部には、温湿度調整用としてサー
モスタット付ヒータ２５とサーモスタット付ファン２６
が取り付けられている。２７はガラス窓で、２８は回り
込みの反射光防止用円筒カバーである。The infrared camera 23, the lens 9 with the remote iris function, the filters 10, 11, 12 and the gear 1
5, the gear 17, the stepping motor 16, the magnet 19, and the switch 20 are housed in the camera housing 24. Inside the camera housing 24, a heater 25 with a thermostat and a fan 26 with a thermostat are used for temperature and humidity adjustment.
Is attached. Reference numeral 27 is a glass window, and 28 is a cylindrical cover for preventing reflected light from wrapping around.
【００２６】撮像した画像の画像入力装置２１への取り
込み、ステッピングモータ１６によるフィルタ１０、１
１、１２の各切り換え、日射量に応じたリモートアイリ
ス機能付レンズ９の絞りの制御、赤外投光器１３、１４
の入り切りは、制御装置１８により行われる。The captured image is taken into the image input device 21, and the filters 10 and 1 by the stepping motor 16 are taken.
Switching of 1 and 12, control of diaphragm of lens 9 with remote iris function according to the amount of solar radiation, infrared projectors 13 and 14
Turning on and off is performed by the control device 18.
【００２７】[0027]
【実施例】次に本発明の実施例を示す。EXAMPLES Examples of the present invention will be described below.
【００２８】撮像対象路面を図３に示す。図３におい
て、２９は乾燥路面、３０は水、３１は氷、３２は雪で
ある。この路面を図４に示す撮像装置である赤外カメラ
により、乾燥路面と水と氷についての吸光の度合いが雪
についての吸光の度合いよりも非常に大きい光の波長で
ある１２００ｎｍを中心波長とするバンドパスフィルタ
である雪分離フィルタ、水と氷についての吸光の度合い
が異なる光の波長である１４３０ｎｍを中心波長とする
バンドパスフィルタである水・氷分離フィルタ及び水と
氷と雪についての吸光の度合いが乾燥路面についての吸
光の度合いより非常に大きい光の波長である１５００ｎ
ｍを中心波長とするバンドパスフィルタである水分分離
フィルタのそれぞれのフィルタを通して撮像し、標本
化、量子化したそれぞれの濃淡画像（以下吸収画像）と
Ｘ軸プロフィールを図５、図６、図７に示す。なお、図
４は図２の装置構成を簡略化して示したものである。吸
収画像の大きさは２５６×２５６画素、濃度階調は２５
６である。図４において、３３は赤外カメラ、３４はリ
モートアイリス機能付レンズ、３５は日射計、３６は雪
分離フィルタ、３７は水分分離フィルタ、３８は水・氷
分離フィルタ、３９、４０は赤外投光器、４１は水分、
４２は路面である。The road surface to be imaged is shown in FIG. In FIG. 3, 29 is a dry road surface, 30 is water, 31 is ice, and 32 is snow. An infrared camera, which is an image pickup device shown in FIG. 4, measures the road surface with a center wavelength of 1200 nm, which is a wavelength of light whose degree of light absorption on a dry road surface and water and ice is much larger than that on snow. A snow separation filter which is a bandpass filter, a water / ice separation filter which is a bandpass filter whose center wavelength is 1430 nm which is a wavelength of light having different degrees of absorption of water and ice, and absorption of water, ice and snow. 1500n, which is a wavelength of light whose degree is much greater than the degree of absorption on a dry road surface
5, 6, and 7 show gray-scale images (hereinafter referred to as absorption images) and X-axis profiles imaged through each filter of a water separation filter which is a bandpass filter having m as a central wavelength, and sampled and quantized. Shown in. Note that FIG. 4 shows a simplified configuration of the apparatus shown in FIG. The size of the absorption image is 256 x 256 pixels, and the density gradation is 25.
It is 6. In FIG. 4, 33 is an infrared camera, 34 is a lens with a remote iris function, 35 is a pyranometer, 36 is a snow separation filter, 37 is a water separation filter, 38 is a water / ice separation filter, and 39 and 40 are infrared projectors. , 41 is water,
42 is a road surface.
【００２９】図５において、４３は雪分離フィルタによ
る吸収画像、４４は乾燥路面、４５は水、４６は氷、４
７は雪である。４９は４３の吸収画像の４８のラインの
プロフィールであり、横軸が４８のラインに対応する画
素（０〜２５５）で、縦軸が濃度階調である。In FIG. 5, 43 is an absorption image by a snow separation filter, 44 is a dry road surface, 45 is water, 46 is ice, 4
7 is snow. 49 is a profile of 48 lines of 43 absorption images, the horizontal axis is pixels (0 to 255) corresponding to the line of 48, and the vertical axis is density gradation.
【００３０】図６において、５１は水・氷分離フィルタ
による吸収画像、５２は乾燥路面、５３は水、５４は
氷、５５は雪、５６は乾燥路面の一部が水に近い濃淡で
現れた部分で、５７は乾燥路面の一部が氷に近い濃淡で
現れた部分である。５９は５１の吸収画像の５８のライ
ンのプロフィールであり、横軸が５８のラインに対応す
る画素（０〜２５５）で、縦軸が濃度階調である。In FIG. 6, reference numeral 51 is an absorption image by a water / ice separation filter, 52 is a dry road surface, 53 is water, 54 is ice, 55 is snow, and 56 is a light and dark part of the dry road surface. In the part, 57 is a part where a part of the dry road surface appears in a shade similar to ice. Reference numeral 59 is a profile of 58 lines of the 51 absorption image, the horizontal axis is pixels (0 to 255) corresponding to the line of 58, and the vertical axis is density gradation.
【００３１】図７において、６２は水分分離フィルタに
よる吸収画像、６３は乾燥路面、６４は水、６５は氷、
６６は雪である。６８は６２の吸収画像の６７のライン
のプロフィールであり、横軸が６７のラインに対応する
画素（０〜２５５）で、縦軸が濃度階調である。In FIG. 7, 62 is an absorption image by a water separation filter, 63 is a dry road surface, 64 is water, 65 is ice,
66 is snow. 68 is a profile of 67 lines of the absorption image of 62, the horizontal axis is pixels (0 to 255) corresponding to the line of 67, and the vertical axis is density gradation.
【００３２】図８は、図５の４３の吸収画像を図５の５
０に示す濃度値１５０にて２値化した画像であり、濃度
値１５０以上が１、１５０未満が０である。明度値１が
黒で、０が白である。７０が乾燥路面で、７１が検知さ
れた雪である。FIG. 8 shows the absorption image of 43 of FIG.
It is an image binarized with a density value 150 shown as 0, where 1 is a density value of 150 or more and 0 is a value less than 150. The brightness value 1 is black and the brightness value 0 is white. 70 is a dry road surface and 71 is snow detected.
【００３３】図９は、図７の６２の吸収画像を図７の６
９に示す濃度値３０にて２値化した画像であり、濃度値
３０以上が０、３０未満が１である。明度値１が黒で、
０が白である。７２が乾燥路面で、７３、７４、７５が
すべて分離された水分である。FIG. 9 shows the absorption image of 62 of FIG. 7 at 6 of FIG.
9 is an image binarized with a density value of 30 shown in FIG. Lightness value 1 is black,
0 is white. 72 is a dry road surface, and 73, 74, and 75 are all separated water.
【００３４】図１０は、図６の５１の吸収画像を図６の
６１に示す濃度値１５にて２値化した画像であり、濃度
値１５以上が０、１５未満が１である。明度値１が黒
で、０が白である。７６が乾燥路面で、７７が分離され
た水で、７８が乾燥路面の一部である。FIG. 10 is an image obtained by binarizing the absorption image 51 of FIG. 6 with the density value 15 shown in 61 of FIG. 6, where the density value 15 or more is 0, and the density value less than 15 is 1. The brightness value 1 is black and the brightness value 0 is white. Reference numeral 76 is a dry road surface, 77 is separated water, and 78 is a part of the dry road surface.
【００３５】図１１は、図６の５１の吸収画像を図６の
６０に示す濃度値３０と６１に示す濃度値１５の２つの
しきい値間で２値化した画像であり、濃度値１５以上３
０未満が１、濃度値１５未満と３０以上が０である。明
度値１が黒で、０が白である。７９が乾燥路面で、８０
が分離された氷で、８１が乾燥路面の一部である。FIG. 11 is an image obtained by binarizing the absorption image of 51 in FIG. 6 between two thresholds of the density values 30 and 61 shown in 60 of FIG. Above 3
Less than 0 is 1 and density values less than 15 and 30 or more are 0. The brightness value 1 is black and the brightness value 0 is white. 79 is a dry road surface, 80
Is the separated ice, and 81 is a part of the dry road surface.
【００３６】図１２は、図８の２値画像と図９の２値画
像の排他的論理和演算結果であり、８２が乾燥路面で、
８３が水、８４が氷である。FIG. 12 shows the exclusive OR operation result of the binary image of FIG. 8 and the binary image of FIG. 9, in which 82 is a dry road surface.
83 is water and 84 is ice.
【００３７】図１３は、図１０の２値画像と図１２の２
値画像の論理積演算結果であり、８５が乾燥路面で、８
６が検知された水である。FIG. 13 shows the binary image of FIG. 10 and the binary image of FIG.
The result of the logical product operation of the value images, where 85 is the dry road surface,
6 is the detected water.
【００３８】図１４は、図１１の２値画像と図１２の２
値画像の論理積演算結果であり、８７が乾燥路面で、８
８が検知された氷である。FIG. 14 shows the binary image of FIG. 11 and the binary image of FIG.
The result of the logical product operation of the value images, where 87 is the dry road surface,
8 is the detected ice.
【００３９】以上、図８の７１、図１３の８６、図１４
の８８がそれぞれ雪、水、氷の検知結果である。As described above, 71 in FIG. 8, 86 in FIG. 13, and FIG.
88 are the detection results of snow, water, and ice, respectively.
【００４０】雪分離フィルタによる吸収画像から２値化
により正確に雪の領域を検知することができた。また、
水・氷分離フィルタによる吸収画像から２値化により水
及び氷を求めることができるが、乾燥路面の一部が混在
した状態で求められる。このノイズとなる乾燥路面の一
部を除去して正確に水及び氷の各領域を検知することが
できた。It was possible to accurately detect the snow region by binarization from the absorption image obtained by the snow separation filter. Also,
Although water and ice can be obtained by binarization from the absorption image obtained by the water / ice separation filter, it is obtained in a state where a part of the dry road surface is mixed. It was possible to accurately detect the water and ice regions by removing a part of the dry road surface that causes this noise.
【００４１】[0041]
【発明の効果】本発明は、以上説明したように、雪、水
と氷、そして水と氷と雪についてのそれぞれの特徴的な
吸光の度合いを持つ光の波長を中心波長に持つバンドパ
スフィルタを通して、日射計に連動したリモートアイリ
ス機能付レンズ搭載カメラにより撮像した３枚の画像に
基づき路面上の水と氷と雪の各分布状態を正確に検知す
る方法である。As described above, the present invention provides a bandpass filter having a central wavelength of light having a characteristic absorption degree for snow, water and ice, and water, ice and snow. This is a method for accurately detecting each distribution state of water, ice, and snow on the road surface based on three images taken by a camera equipped with a lens having a remote iris function linked to a pyranometer.
【００４２】日射計に連動したリモートアイリス機能付
レンズ搭載カメラを用いていることから、日射の変動に
影響されないで安定して検知のために必要な３枚の画像
を得ることができる。Since the camera equipped with the lens having the remote iris function linked to the pyranometer is used, the three images necessary for detection can be stably obtained without being affected by the fluctuation of the solar radiation.
【００４３】したがって、本発明による方法により路面
上の水、氷、雪の各分布情報を得ることができる非接触
方式の広領域凍結検知装置を提供することができ、従来
の電極式の水分センサに替えてロードヒーティング制御
のための凍結検知センサとして用いることができ、凍結
検知の精度と信頼性が飛躍的に向上する。この結果、正
確な制御が可能になり、制御路面上に凍結が生じること
がなく、また過剰なヒーティングによる無駄なエネルギ
ー消費がなくなり、冬期の安全な道路の確保と多大なエ
ネルギー節減の効果がある。Therefore, it is possible to provide a non-contact type wide area freezing detection device which can obtain the distribution information of water, ice and snow on the road surface by the method according to the present invention, and the conventional electrode type moisture sensor. Instead, it can be used as a freeze detection sensor for load heating control, and the accuracy and reliability of freeze detection are dramatically improved. As a result, accurate control becomes possible, freezing does not occur on the control road surface, wasteful energy consumption due to excessive heating is eliminated, and a safe road in winter and a great energy saving effect are achieved. is there.
【００４４】また、ＣＣＤカメラや人の目では判断が難
しいブラックアイスバーンの検知が可能であることか
ら、冬期の峠などの道路状態監視装置として用いること
ができ、その検知情報は、冬期のＩＴＳ（Ｉｎｔｅｌｌ
ｉｇｅｎｔ Ｔｒａｎｓｐｏｒｔ Ｓｙｓｔｅｍｓ）情
報としても有効であり、冬期交通安全に大きな効果があ
る。Further, since it is possible to detect a black ice burn which is difficult to judge by a CCD camera or human eyes, it can be used as a road condition monitoring device for a winter pass, etc., and the detection information is used for the winter ITS. (Intel
It is also effective as information about the Intelligent Transport Systems and has a great effect on winter road safety.
【００４５】また、凍結路面発生を防ぐためにロードヒ
ーティングの代わりとして行われる融雪剤散布のための
凍結情報提供が可能であり、非常に効率的な散布が可能
になり、無駄な散布を節減する効果がある。Further, it is possible to provide freezing information for the snow-melting agent spraying which is carried out as an alternative to the road heating in order to prevent the generation of a frozen road surface, which enables a very efficient spraying and saves unnecessary spraying. effective.
【図１】水、氷、雪を乾燥路面から分離し、検知する手
順である。FIG. 1 is a procedure for separating and detecting water, ice and snow from a dry road surface.
【図２】水と氷と雪の検知装置の構成である。FIG. 2 is a configuration of a water, ice, and snow detection device.
【図３】実施例における撮像対象路面である。FIG. 3 is a road surface to be imaged in the embodiment.
【図４】本発明の検知を実現するための撮像装置の構成
の説明図である。FIG. 4 is an explanatory diagram of a configuration of an image pickup apparatus for realizing the detection of the present invention.
【図５】雪分離フィルタによる吸収画像とこの吸収画像
の中央横ラインのプロフィールである。FIG. 5 is a profile of an absorption image by a snow separation filter and a center horizontal line of the absorption image.
【図６】水・氷分離フィルタによる吸収画像とこの吸収
画像の中央横ラインのプロフィールである。FIG. 6 is an absorption image by a water / ice separation filter and a profile of a center horizontal line of the absorption image.
【図７】水分分離フィルタによる吸収画像とこの吸収画
像の中央横ラインのプロフィールである。FIG. 7 is a profile of an absorption image by a water separation filter and a center horizontal line of the absorption image.
【図８】雪の検知結果である。FIG. 8 is a result of snow detection.
【図９】水、氷、雪の水分の検知結果である。FIG. 9 shows detection results of water, ice and snow.
【図１０】２値化により求められた水と乾燥路面の一部
である。FIG. 10 shows water and a part of a dry road surface obtained by binarization.
【図１１】２値化により求められた氷と乾燥路面の一部
である。FIG. 11 is a portion of ice and a dry road surface obtained by binarization.
【図１２】排他的論理和演算により求められた水と氷で
ある。FIG. 12 shows water and ice obtained by an exclusive OR operation.
【図１３】論理積により求められた水の検知結果であ
る。FIG. 13 is a water detection result obtained by a logical product.
【図１４】論理積により求められた氷の検知結果であ
る。FIG. 14 is an ice detection result obtained by a logical product.
フロントページの続き (51)Int.Cl.7 識別記号 ＦＩ テーマコート゛(参考） Ｈ０４Ｎ 7/18 Ｈ０４Ｎ 7/18 Ｋ (71)出願人 501497150 本間 稔規 北海道札幌市北区北19条西11丁目１番地 北海道立工業試験場内 (71)出願人 501497183 宮崎 俊之 北海道札幌市北区北19条西11丁目１番地 北海道立工業試験場内 (71)出願人 595095696 札幌総合情報センター株式会社 北海道札幌市中央区北一条西３丁目３番地 (72)発明者 波 通隆 北海道札幌市北区北19条西11丁目１番地 北海道立 工業試験場内 (72)発明者 本間 稔規 北海道札幌市北区北19条西11丁目１番地 北海道立 工業試験場内 (72)発明者 宮崎 俊之 北海道札幌市北区北19条西11丁目１番地 北海道立 工業試験場内 (72)発明者 池上 真志樹 北海道札幌市豊平区月寒東２条17丁目２番 １号 独立 行政法人産業技術総合研究所 北海道センター内 (72)発明者 磯田 和志 北海道江別市対雁２番１号 北海道電力株 式会社総合研究所内 (72)発明者 村上 康之 北海道江別市対雁２番１号 北海道電力株 式会社総合研究所内 (72)発明者 金村 直俊 北海道札幌市中央区北１条西３丁目３番地 札幌総合情 報センター株式会社内 (72)発明者 安藤 浩司 北海道札幌市中央区北１条西３丁目３番地 札幌総合情 報センター株式会社内 Ｆターム(参考） 2G059 AA01 AA05 BB08 CC09 EE02 GG03 GG10 HH01 JJ02 JJ11 KK04 MM01 MM09 5C022 AA01 AB12 AC55 AC56 AC74 5C054 CA05 FC05 FC12 HA38 Front page continuation (51) Int.Cl. 7 identification code FI theme code (reference) H04N 7/18 H04N 7/18 K (71) Applicant 501497150 Minoru Honma Kita-ku Kita-ku Kita-ku Nishi 11-chome 1 1 Address Inside the Hokkaido Industrial Research Institute (71) Applicant 501497183 Toshiyuki Miyazaki 11-1 Nishi Kita-ku, Kita-ku, Sapporo-shi, Hokkaido 1-in-1 Hokkaido Industrial Research Institute (71) Applicant 595095696 Sapporo General Information Center Co., Ltd. Ichijo Nishi 3-chome 3 (72) Inventor Natsu Takashi Kita-ku Kita-ku, Sapporo, Hokkaido Kita 19-11 West 11-chome, Hokkaido Industrial Test Station (72) Inventor Minoru Honma Kita-ku Kita-ku Kita-ku, Sapporo 11 Hokkaido 11 1-chome, Hokkaido Industrial Test Station (72) Inventor Toshiyuki Miyazaki 11-chome, Kita-ku, Kita-ku, Sapporo 11-chome, Hokkaido 1-chome Hokkaido Industrial Test Station (72) Inventor Masashi Ikegami, Tsukikanto 2 Article, Toyohira-ku, Sapporo, Hokkaido 17-2-1 No. 1 National Institute of Advanced Industrial Science and Technology Hokkaido Center (72) Inventor Kazushi Isoda No. 2 vs. Gan, Ebetsu City, Hokkaido Research Institute of Hokkaido Electric Power Company (72) Inventor Yasuyuki Murakami No. 2 vs. Ebisu City, Hokkaido Research Center of Hokkaido Electric Power Company (72 ) Inventor Naotoshi Kanamura 3-1, Kita 1-jo Nishi, Chuo-ku, Sapporo, Hokkaido Within Sapporo General Information Center Co., Ltd. (72) Inventor Koji Ando 3-chome, Kita 1-jo Nishi 3, Chuo-ku, Sapporo, Hokkaido Sapporo General Information Center Company F term (reference) 2G059 AA01 AA05 BB08 CC09 EE02 GG03 GG10 HH01 JJ02 JJ11 KK04 MM01 MM09 5C022 AA01 AB12 AC55 AC56 AC74 5C054 CA05 FC05 FC12 HA38
Claims (2)
吸光の度合いが異なる光の波長を中心波長に持つバンド
パスフィルタ（以下水・氷分離フィルタ）と、水と氷と
乾燥路面のそれぞれについての吸光の度合いは似ている
が、それら吸光の度合いと雪についての吸光の度合いと
が異なる光の波長を中心波長に持つバンドパスフィルタ
（以下雪分離フィルタ）と、水と氷と雪のそれぞれにつ
いての吸光の度合いは似ているが、それら吸光の度合い
と乾燥路面についての吸光の度合いが異なる光の波長を
中心波長に持つバンドパスフィルタ（以下水分分離フィ
ルタ）の３枚のバンドパスフィルタを通して、日射量に
応じてカメラ絞りが、昼夜に関わらず常にこれら３つの
波長の光を含む赤外投光器の光の下のみで撮像した画像
になるように制御されるこれら３つの波長に感度を持つ
リモートアイリス機能付レンズ搭載カメラによりとらえ
て撮像した３枚の画像を標本化、量子化した３枚のディ
ジタル画像に基づき、雪分離フィルタを通して得たディ
ジタル画像から２値化により得た雪の２値画像（以下雪
画像）が雪の検知結果になり、この雪画像と水分分離フ
ィルタを通して得たディジタル画像から２値化により得
た水・氷・雪の２値画像（以下水分画像）との排他的論
理和の演算を行った結果の水・氷の２値画像（以下水氷
演算画像）を得て、水・氷分離フィルタを通して得たデ
ィジタル画像から２値化により得た水の２値画像（以下
水画像）と水氷演算画像との論理積の演算を行った結果
の２値画像が水の検知結果になり、水・氷分離フィルタ
を通して得たディジタル画像から２値化により得た氷の
２値画像（以下氷画像）と水氷演算画像との論理積の演
算を行った結果の２値画像が氷の検知結果になることを
特徴とする路面上の水と氷と雪の検知方法1. A bandpass filter (hereinafter referred to as a water / ice separation filter) having a central wavelength of light having a different degree of absorption of water and a degree of absorption of ice, and water, ice and a dry road surface, respectively. Although the degree of light absorption is similar, the degree of light absorption and the degree of light absorption for snow are different, and a band-pass filter (hereinafter referred to as a snow separation filter) having a wavelength of light as a central wavelength and water, ice, and snow, respectively. The degree of light absorption is similar, but the degree of light absorption is different from the degree of light absorption on the dry road surface. Through three bandpass filters of bandpass filter (hereinafter referred to as water separation filter) having the wavelength of light as the central wavelength. , Control the camera diaphragm according to the amount of solar radiation so that the image is always taken only under the light of the infrared projector that includes light of these three wavelengths regardless of day or night. 2 digital images obtained through a snow separation filter based on 3 digital images sampled and quantized from 3 images captured by a camera with a lens equipped with a remote iris function that is sensitive to these 3 wavelengths. The binary image of snow obtained by binarization (hereinafter referred to as snow image) becomes the detection result of snow, and the binary image of water, ice and snow obtained by binarization from this snow image and the digital image obtained through the water separation filter. A binary image of water and ice (hereinafter referred to as water ice calculation image) obtained by performing an exclusive OR operation with an image (hereinafter referred to as water image) is obtained, and the binary image is obtained from the digital image obtained through the water / ice separation filter. The digital image obtained through the water / ice separation filter is the binary image that is the result of the logical product operation of the binary image of water (hereinafter referred to as water image) 2 from the image Water on the road surface, characterized in that the binary image of the result of the logical product operation of the binary image of ice obtained by computerization (hereinafter referred to as ice image) and the water ice calculation image is the detection result of ice. How to detect ice and snow
分離フィルタと水分分離フィルタの３枚のフィルタを切
り換えながら、それぞれのフィルタを通してリモートア
イリス機能付レンズ搭載カメラによりとらえて撮像した
３枚の画像から雪画像と水分画像と水画像と氷画像を得
て路面上の水と氷と雪を検知する方法により構築する路
面上の水と氷と雪の検知装置2. A water-ice separation filter, a snow separation filter, and a water separation filter according to claim 1, which are switched between three filters and captured by a camera equipped with a lens having a remote iris function through each filter, and imaged. A device for detecting water, ice, and snow on the road surface, which is constructed by a method for detecting water, ice, and snow on the road surface by obtaining snow images, moisture images, water images, and ice images from a single image
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| JP2001395440A JP3733434B2 (en) | 2001-11-19 | 2001-11-19 | Detection method of water, ice and snow on road surface and detection device of water, ice and snow on road surface |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2001395440A JP3733434B2 (en) | 2001-11-19 | 2001-11-19 | Detection method of water, ice and snow on road surface and detection device of water, ice and snow on road surface |
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| Publication Number | Publication Date |
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| JP2003156430A true JP2003156430A (en) | 2003-05-30 |
| JP3733434B2 JP3733434B2 (en) | 2006-01-11 |
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| JP (1) | JP3733434B2 (en) |
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|---|---|---|---|---|
| WO2006011571A1 (en) * | 2004-07-28 | 2006-02-02 | Kyocera Corporation | Light source and endoscope equipped with this light source |
| JP2008191144A (en) * | 2007-01-11 | 2008-08-21 | Japan Aerospace Exploration Agency | Optical spectropolarimeter |
| JP2012026927A (en) * | 2010-07-26 | 2012-02-09 | Astron Inc Kk | Weather measuring apparatus |
| KR101175256B1 (en) | 2010-08-18 | 2012-08-21 | 한국해양연구원 | Method and apparatus for measuring the thickness of ice |
| KR101349497B1 (en) * | 2012-08-13 | 2014-01-09 | 한국해양과학기술원 | Measurement method of sea ice thickness by using video camera |
| GB2549387A (en) * | 2016-04-07 | 2017-10-18 | Ford Global Tech Llc | System and method for inspecting road surfaces |
| FR3066592A1 (en) * | 2017-05-16 | 2018-11-23 | Themacs Ingenierie | DEVICE ADAPTED TO BE ON BOARD A VEHICLE FOR THERMAL CARTOGRAPHY |
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| CN113748331A (en) * | 2019-04-25 | 2021-12-03 | 罗伯特·博世有限公司 | Method and device for determining the solid form of water on a roadway surface |
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2001
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006011571A1 (en) * | 2004-07-28 | 2006-02-02 | Kyocera Corporation | Light source and endoscope equipped with this light source |
| JP2008191144A (en) * | 2007-01-11 | 2008-08-21 | Japan Aerospace Exploration Agency | Optical spectropolarimeter |
| JP2012026927A (en) * | 2010-07-26 | 2012-02-09 | Astron Inc Kk | Weather measuring apparatus |
| KR101175256B1 (en) | 2010-08-18 | 2012-08-21 | 한국해양연구원 | Method and apparatus for measuring the thickness of ice |
| KR101349497B1 (en) * | 2012-08-13 | 2014-01-09 | 한국해양과학기술원 | Measurement method of sea ice thickness by using video camera |
| GB2549387A (en) * | 2016-04-07 | 2017-10-18 | Ford Global Tech Llc | System and method for inspecting road surfaces |
| FR3066592A1 (en) * | 2017-05-16 | 2018-11-23 | Themacs Ingenierie | DEVICE ADAPTED TO BE ON BOARD A VEHICLE FOR THERMAL CARTOGRAPHY |
| US11245875B2 (en) | 2019-01-15 | 2022-02-08 | Microsoft Technology Licensing, Llc | Monitoring activity with depth and multi-spectral camera |
| CN113748331A (en) * | 2019-04-25 | 2021-12-03 | 罗伯特·博世有限公司 | Method and device for determining the solid form of water on a roadway surface |
| CN113390794A (en) * | 2020-03-13 | 2021-09-14 | 弗劳恩霍夫应用研究促进协会 | Device for detecting water on a surface and method for detecting water on a surface |
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