EP4371082A1 - Überwachung von definierten optischen mustern mittels objekterkennung und maschinellen lernens - Google Patents
Überwachung von definierten optischen mustern mittels objekterkennung und maschinellen lernensInfo
- Publication number
- EP4371082A1 EP4371082A1 EP22753624.0A EP22753624A EP4371082A1 EP 4371082 A1 EP4371082 A1 EP 4371082A1 EP 22753624 A EP22753624 A EP 22753624A EP 4371082 A1 EP4371082 A1 EP 4371082A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- image data
- characters
- recognized
- character
- camera
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/5846—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using extracted text
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/63—Scene text, e.g. street names
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/02—Recognising information on displays, dials, clocks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/06—Recognition of objects for industrial automation
Definitions
- the invention relates to a system for monitoring technical installations using object recognition of defined optical patterns. Furthermore, a method for monitoring technical systems by means of object recognition of defined optical patterns is described.
- One object of the present patent application is therefore to propose such an automatic system for the optical monitoring of technical systems.
- a simple method for optical monitoring of technical systems is proposed.
- Signs that are attached to parts of technical systems in an environment that are to be monitored, at least one camera that captures image data of the area and provides it with spatial coordinates and the time of recording, an image database in which the image data is stored, a symbol library in which a large number of Characters and rules assigned to them are stored, and an object recognition unit, which is designed to recognize characters in the image data and to compare them with the characters stored in the character library, with a spatial coordinate being assigned to the character when a character is recognized in the image data, a comparison with earlier image data of the environment is carried out and an alarm is triggered in the event of non-compliance with a rule associated with the recognized character.
- monitoring is not limited to the condition of a specific component, such as a screw connection, but instead to monitoring signs attached to the systems.
- the characters are simple optical patterns that can be based, for example, on basic geometric shapes. These characters form a defined sign language in which each character is assigned a rule that can be checked visually. These rules can concern, for example, the state of rotation or the visibility of a character.
- the signs are not limited in terms of their size and design, so they can be adapted to a wide range of applications and environmental conditions.
- the signs are attached to the parts of a technical installation to be monitored. They can be designed as stickers or they can be painted on using a stencil.
- the signs are applied to parts for which the rules assigned to the signs are to apply. For example, a sign can be attached to a screw connection that should not loosen, to which a rule is assigned that states that the sign must not rotate in the loosening direction of the screw connection.
- At least one camera is set up to monitor the system. Multiple cameras can also be used to get different perspectives of the system. It can be a camera with conventional optical sensors or a device that, depending on the special requirements of the environment, can take pictures outside the optical spectrum, for example in the infrared range.
- the camera captures image data of the technical system at specified intervals. This data is also provided with information about the recording location, the spatial coordinates and the recording time.
- the system also includes an image database.
- the image data recorded by the at least one camera are stored in this. In this way the comparison of image data from different recording times is made possible.
- the database can be created as a local storage medium or connected to the rest of the system via a network.
- the character library describes the defined character language. It comprises a storage medium with a database in which examples of all the characters used are stored. At least one rule is assigned to each character in the character library.
- the character library can be extensible.
- the core of the system is an object recognition unit that processes the image data captured by the camera.
- the object recognition engine uses a combination of static and dynamic algorithms to recognize the defined characters in the image data. It recognizes signs based on their shape, color and reflection properties.
- the object recognition unit is also designed to record the state of the recognized character, ie rotation or soiling, for example. A variety of different methods can be used. The choice of a method for recognizing the characters can be made depending on the properties of the environment and the properties of the defined characters.
- the object detection unit can be a computer running specialized software. However, it can also be a network of computers or specialized electronics.
- the object recognition unit recognizes a character stored in the character library in the image data, it determines its spatial coordinates from the image data. In addition, a comparison is made with image data recorded in the same environment at an earlier point in time. If the sign is recorded in this environment for the first time, it is a sign that was newly attached to the system to be monitored. The image data is then saved in the image database and is available there for later comparisons. If a character is recognized for the second or repeated time in the image data at a spatial coordinate, the object recognition unit checks compliance with the rule stored for this character in the character library. If the rule is observed in the newly acquired image data, no further action is taken. However, if the object recognition unit detects a change in the state of the character, which contradicts the stored rule, the system issues an alarm signal. Based on the alarm signal, the system can be serviced or other necessary steps can be taken.
- the system can easily monitor the status of the technical installation as well.
- the system is extremely flexible because the character library can be expanded at any time and new characters can be added to the system. Since a large number of different rules can be assigned to the characters, the system is not limited to monitoring a single status. Since the system works completely automatically, monitoring can take place at any interval or continuously.
- the at least one camera can be permanently installed. This simplifies the determination of the spatial coordinates of the image data and the characters recognized therein, since the position of the camera and its viewing angle can be precisely defined.
- one or more surveillance cameras can be installed in the vicinity of the technical system to be monitored in order to capture different perspectives.
- the at least one camera can also be mobile.
- it can be attached to a drone or a robotic arm, or it can be embodied as part of a handheld device such as a smartphone.
- a handheld device such as a smartphone.
- Such a design of the camera allows technical systems to be checked at fixed intervals, even in positions that are difficult to access. Nevertheless, several cameras do not have to be permanently installed to monitor larger systems.
- the system can be designed to work autonomously.
- the at least one camera can include a device for determining the position, a device for determining the recording angle and a device for determining the distance.
- a device for position determination is used to provide the captured image data with spatial coordinates. This is particularly the case with mobile ments of the camera where the position of the camera is not fixed is necessary. For example, it can simply be a system for determining position via GPS. The distances between the characters recognized in the area can be determined using a device for distance determination
- a device for determining the recording angle can, for example, be based on acceleration sensors or on a gyrocompass.
- the distance measuring device can include a laser distance meter, for example. This can be used to reliably determine the distances between the at least one camera and the parts of the technical system from which image data are recorded.
- the distance measurement can be used to reliably determine the distances between the at least one camera and the parts of the technical system from which image data are recorded.
- the signs attached to a technical system can be designed in such a way that their color and reflective properties make them stand out particularly well from their surroundings. This simplifies the recognition of the characters by the object recognition unit. For example, colors can be used that do not otherwise occur in the environment, or
- the characters can be designed as stickers, which are made of materials that achieve particularly good visibility through their surface properties.
- the sign language used can be defined in such a way that the associated
- BO rules not only affect individual characters, but can also relate several characters to one another. Rules regarding the distance or angle between two characters can also be set up in this way. The character library then assigns these rules to the corresponding characters.
- the object recognition unit can be based on machine learning Use the model to recognize the specified characters in the image data.
- Such a model must first be trained to recognize the characters, but then offers greater flexibility with regard to the recording angles and environmental influences under which the characters can be recognized. While the characters in the image data can be recognized by such a model, the recognized characters can still be assigned to the characters and rules stored in the character library by static algorithms.
- the entire system for monitoring technical installations can be designed as a computer that includes the object recognition unit, the image database and the character library, or runs corresponding programs, and is connected to at least one camera for capturing the image data.
- a computer can be available locally at the monitoring site.
- it can also be a central computer that receives the data from a large number of static or mobile cameras via a network and thus enables the simultaneous monitoring of several technical systems.
- a method for monitoring technical systems comprises the following steps: optical detection of an environment using a camera to produce image data,
- Evaluation of the recognized characters whereby if there are no characters in the image data from earlier recording times or a recognized character was not recognized in the image data from earlier recording times, the image data are stored, if the recognized characters were recognized in the image data of earlier acquisition times and no change is recognized that violates a rule assigned to the recognized characters, no further actions are carried out if the recognized characters were recognized in the image data of earlier acquisition times and a change is recognized that violates a rule associated with the recognized characters, an alarm is triggered.
- image data of the surroundings are captured by a camera.
- This image data is forwarded to the object recognition unit, where it is prepared for further processing in a second step.
- they are provided with spatial coordinates and the time of recording.
- the object recognition unit carries out the algorithms for recognizing the defined characters in the image data.
- the recognized characters are given spatial coordinates, which are calculated from the image data.
- the recognized characters are assigned to the characters stored in the character library. Rules are assigned to the characters stored in the character library. If a character is recognized in the image data and successfully assigned to a character defined in the character library, it is also known which rule should apply to the recognized character.
- the image data are compared with image data recorded at the same spatial coordinates from earlier recording times.
- the new image data is saved in order to be able to compare them at a later point in time and thus check compliance with the rules of the recognised to allow signs.
- the known character was also recognized in the image data of earlier recording times, a check is made as to whether the state of the character recognized in the new image data corresponds to the rules assigned to the character. If so, no action is taken and monitoring can continue continuously or at a later time. However, if a change to the character is detected that violates a rule assigned to the character hits, an alarm signal is output.
- the second step of preparing the image data for further processing can also include providing the image data with data that
- the second step in preparing the image data for further processing can also include first pre-processing and filtering the image data.
- the contrast can be adjusted and certain colors can be filtered, or algorithms can be used for noise reduction or edge detection.
- the defined characters In order to also make the defined characters more easily recognizable in the recognition step, they can be designed in such a way that they stand out particularly well from their surroundings due to their coloring and reflection properties. For example, colors can be used that are in order
- the characters can be designed as stickers, which consist of materials that achieve particularly good visibility due to their surface properties.
- the character library can also assign rules to the characters that relate several characters to one another. In this way, the rules can also affect relationships between two characters, such as distance or angle.
- a model based on machine learning can be used to recognize the specified characters in the image data.
- Such a model must first be trained to recognize the characters, but then offers greater flexibility
- the recognized characters can still be assigned to the characters and rules stored in the character library using static algorithms.
- the system described, as well as the method for monitoring technical installations enable cost-effective automatic monitoring of technical installations.
- the system and the method can therefore find a wide range of possible applications when it comes to improving the safety of technical systems and making their maintenance easier to plan.
- such a system can be used in places that would be difficult or dangerous for human personnel to access.
- FIG. 1 shows a schematic overview of a system according to the invention for monitoring technical installations
- FIG. 3 shows the sequence of the method for monitoring technical installations.
- a sign 1 is attached to a technical installation or in the vicinity of a technical installation.
- the camera captures the surroundings of the technical system op- table.
- the camera 2 can be permanently installed in the area or mobile, for example as part of a drone. If it is permanently installed, its position is known and its recording angle and its distance from various parts of the technical system can easily be calculated or measured. If the camera 2 is mobile, it is necessary to additionally determine the position of the camera 2, the recording angle and the distance from the detected objects.
- Both a static and a mobile version of the camera 2 can therefore have a device for determining position 6, for example a GPS receiver, a device for determining the recording angle 7, for example an acceleration sensor, and a device for measuring distance 8, for example a laser range finder , feature.
- the distance measurement can also be determined via the focus setting of the camera 2, for example.
- the image data captured by the camera 2 can be assigned precise information from which the spatial coordinates of the objects captured in the image data can be calculated in the further course.
- An internal clock also records the recording time of the image data.
- the image data are forwarded from the camera 2 to an object recognition unit 3 .
- this is part of a computer 9 together with the image database 4 and the character library 5.
- these elements do not have to be part of a single computer; it can also be a network of computers or specialized circuits at different locations.
- the data can be transmitted from the camera 2 to the object recognition unit 3 via a cable connection, but it can also be done via wireless data transmission.
- the data can also be transmitted via larger networked structures such as the Internet.
- a pre-processing of the image data can take place in the object recognition unit 3 .
- Various algorithms can be applied to the image data in order to remove disruptive effects such as noise from the data or to emphasize elements that are advantageous for object recognition, such as edges. It can also make sense to apply certain color filters to the captured image data if the characters to be recognized have a corresponding have de coloring.
- the actual object recognition can then be done using a machine learning-based model that is defined with the help of the
- the object recognition unit uses the additional information in the image data to determine the spatial coordinates of the recognized character 1.
- the character library 5 includes a large number of character definitions and assigns rules to them. Compared to those in Character Library 5
- the recognized character 1 can be recognized as one of the defined characters and a rule can also be assigned to it.
- image data are stored in the image database 4 to enable later comparison.
- a defined sign language is used to mark the technical systems to be monitored and to recognize their states. in figure
- BO 2a, 2b and 2c show examples of such characters.
- the examples shown are all based on a circle provided with a section, but other basic geometric shapes are also conceivable for such a character language that are well suited for object recognition. In order to enable reliable object recognition, the characters are set out as simply as possible
- the signs must also be designed in terms of their shape and color in such a way that they are easily recognizable in their surroundings and at the same time clearly distinguishable from each other are to be distinguished. Rules are assigned to each character or pairs or groups of characters, which are used to evaluate the state of the technical system to be monitored.
- the character shown in FIG. 2a is assigned the rule "This character must not rotate about the normal of its plane”. If this character is attached to a screw connection, the system effectively monitors whether this is rotating and whether it is possible.
- the shape of the sign makes it particularly easy to detect rotation.
- Fig. 2b shows a combination of two signs that are related to each other by the rule "The angle between these two signs must not change". These two signs can thus be applied to two components of a plant that do not The angular relationship between the two characters can be determined by the double dashes in the circles of the characters.
- the characters can also have non-geometric meanings.
- the rule "This sign must always be visible after its first detection” can be applied to the sign shown in FIG. 2c. This sign can thus be used to monitor the contamination of a surface, for example by a leak be used to detect improper movement of a component or to mark an emergency exit that must be kept clear.
- a large number of states of a technical installation can be monitored that are otherwise difficult to control automatically or require very specific sensors.
- a system that uses such characters and object recognition based on machine learning is extremely flexible. For example, several characters can be recognized simultaneously in image data and related to one another.
- a character library 5 can be expanded and new characters can be attached to existing technical installations without great technical effort.
- the sequence of the method for monitoring technical systems is to be described again with reference to the diagram in FIG. 3 .
- a first step S1 an image is captured by a camera 2. Such image acquisition can be continuous or at fixed intervals.
- the image data provided by the camera 2 are forwarded to an object recognition unit 3, which carries out steps S2 and S3.
- the image data are first prepared for further processing.
- This preparation S2 includes providing the image data with information about the time of recording, the spatial coordinates of the recording location, recording angle and the distance of the recorded objects from the camera.
- the preparation S2 of the image data can also include pre-processing in which disturbing effects are filtered out of the image data or algorithms for edge detection are applied to them in order to prepare the image data for the actual object detection in step S3.
- the character recognition S3 may be based on a machine learning model that has been trained to recognize the defined characters under different circumstances. In this way, a particularly good detection can be guaranteed with a high flexibility of the system at the same time.
- step S4 the recognized character is assigned to a character stored in the character library 5 .
- the recognized character is assigned to a character stored in the character library 5 .
- Such an assignment can be made using static algorithms.
- a rule is assigned to the recognized character.
- step S5 a comparison is made between the captured image data in which a character was recognized and image data that were captured at an earlier recording date at the same spatial coordinates. If there is no previous image data or if the recognized character was not captured in this data, this means that the procedure for this environment was carried out for the first time or a sign has been newly affixed to a facility.
- the recorded image data are therefore stored in the image database 4 in step S6.1 in order to enable a later comparison. If the character recognized in the new image data was already recognized in image data from earlier recording times, but no change can be detected that violates the rule assigned to the character, then there is no need for action in step S6.2. However, if the comparison of the new and old image data reveals that a change has taken place in the technical system that violates the rule assigned to the character, an alarm is triggered in step S6.3.
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Abstract
Description
Claims
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102021207568.1A DE102021207568A1 (de) | 2021-07-15 | 2021-07-15 | Überwachung von definierten optischen Mustern mittels Objekterkennung und neuronaler Netzwerken |
| PCT/EP2022/069797 WO2023285621A1 (de) | 2021-07-15 | 2022-07-14 | Überwachung von definierten optischen mustern mittels objekterkennung und maschinellen lernens |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| EP4371082A1 true EP4371082A1 (de) | 2024-05-22 |
Family
ID=82850425
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP22753624.0A Pending EP4371082A1 (de) | 2021-07-15 | 2022-07-14 | Überwachung von definierten optischen mustern mittels objekterkennung und maschinellen lernens |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US20240338945A1 (de) |
| EP (1) | EP4371082A1 (de) |
| CA (1) | CA3226740A1 (de) |
| DE (1) | DE102021207568A1 (de) |
| WO (1) | WO2023285621A1 (de) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5616478B1 (ja) | 2013-04-18 | 2014-10-29 | ファナック株式会社 | ワークを搬送するロボットを備えるロボットシステム |
| DE102015007624A1 (de) | 2015-06-16 | 2016-12-22 | Liebherr-Components Biberach Gmbh | Verfahren zum Montieren von elektrischen Schaltanlagen sowie Montagehilfsvorrichtung zum Erleichtern der Montage solcher Schaltanlagen |
| WO2018013439A1 (en) * | 2016-07-09 | 2018-01-18 | Grabango Co. | Remote state following devices |
| DE102016214705B4 (de) | 2016-08-08 | 2022-05-05 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Einrichtung zur Überwachung von mechanischen Verbindungsstellen einer Anlage |
| DE202017001227U1 (de) | 2017-03-07 | 2018-06-08 | Kuka Deutschland Gmbh | Objekterkennungssystem mit einem 2D-Farbbildsensor und einem 3D-Bildsensor |
-
2021
- 2021-07-15 DE DE102021207568.1A patent/DE102021207568A1/de active Pending
-
2022
- 2022-07-14 US US18/579,388 patent/US20240338945A1/en active Pending
- 2022-07-14 WO PCT/EP2022/069797 patent/WO2023285621A1/de not_active Ceased
- 2022-07-14 EP EP22753624.0A patent/EP4371082A1/de active Pending
- 2022-07-14 CA CA3226740A patent/CA3226740A1/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023285621A1 (de) | 2023-01-19 |
| US20240338945A1 (en) | 2024-10-10 |
| CA3226740A1 (en) | 2023-01-19 |
| DE102021207568A1 (de) | 2023-01-19 |
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