INLOC Robotics

Digital fingerprint reader

A fingerprint reader was until recently quite an exotic technology in the real world, where we watched futuristic movies with these types of devices.In recent years, however, scanners have started to appear everywhere: high-security buildings, mobile phones, office entrances, and even on PC keyboards.

Fingerprints are a unique marker for each person.While two prints may look basically the same, advanced software can detect sharp and clear differences.

In this article, we will talk more about the fingerprint detector and the different types out there.

What is a fingerprint reader?

Fingerprint sensors are electronic devices that allow us to identify our fingerprints.This process is mainly composed of reading, saving and identifying the fingerprint.Each imprint of our fingers are different from each other, and of course, different from anyone else.

How does the fingerprint reader work?

A fingerprint scanner system has two basic functions.On the one hand, you need to obtain an image of your finger and on the other, you must determine if the pattern of ridges and valleys in that image matches the pattern of ridges and valleys in previously scanned and saved images.

Fingerprint scanning is a form of physiological biometrics that analyzes your physical characteristics to authenticate your identity.Basically, it recognizes that your fingerprint belongs to you and no one else.

We all have unique identification marks on our fingers that are used to create a fingerprint.These cannot be changed or deleted, so they are a good indicator of identity for security procedures.

Keep in mind that some scanners rely on light, some on electricity, and some on sound to map the ridges and valleys of your fingers.

Types of fingerprint scanners

There are four types of fingerprint scanner:

Optical scanner
Capacitance scanner
Ultrasonic scanner
Thermal scanner

Optical scanners take a visual image of the fingerprint using a digital camera. The set of pixels form an image of the scanned scene (in this case, a finger). Subsequently, the captured fingerprint is compared with the registered fingerprints.

Capacitive or CMOS scanners use capacitors and thus form an image of the fingerprint through electrical current. This type of scanner tends to stand out in terms of accuracy. These types of scanners are usually more compact than optical devices.

Ultrasound fingerprint scanners use high-frequency sound waves to penetrate the epidermal (outer) layer of the skin. The waves are then reflected off the sensor which are then analyzed to create a digital image of the fingerprint.

Finally, thermal scanners detect temperature differences at the contact surface, between the ridges and valleys of fingerprints.

Advantages and disadvantages of a fingerprint reader

Fingerprint scanners have a number of advantages over other systems.Below we name a series of them.

Physical attributes are much more difficult to counterfeit than identity cards.
You can’t guess a fingerprint pattern like you can guess a password.
You can’t lose your fingerprints, iris, or voice like you can lose an access card.
You can’t forget your fingerprints like you can forget a password.

But as effective as it is, certainly a fingerprint sensor is not foolproof and they have major disadvantages.Optical scanners cannot always distinguish between the image of a finger and the finger itself, and capacitive scanners can sometimes be fooled by a cast of a person’s finger.

Even in the worst case, a criminal could even cut off someone’s finger to get past the scanner’s security system.

To make these security systems more reliable, a good idea is to combine biometric analysis with a conventional means of identification, such as a password (in the same way that an ATM requires a bank card and a PIN code).

Conclusions

There are several ways that a security system can verify that someone is an authorized user.

In general, to overcome a system you look for “what you have“, such as an identity card with a magnetic stripe.On the other hand, it looks for “what you know” and requires you to enter a password or a PIN number.Or finally, “who are you“, where you look for physical evidence that really confirms your identity and it is here where we find a voice pattern, an iris or specifically the main topic of this article, a fingerprint.

We hope this article has been useful to you.If you have an engineering project in your hands and you think we can help you, here is the link where you can contact us and explain more about it.

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Segmentation and decoding of text in an image

In the world of computer vision, it is very common to find images where, among other objects or environments, text also appears. Sometimes, it is of special interest to be able to read it, so its segmentation from the rest of the image is of great importance.

The degree of difficulty of text detection varies greatly depending on the environment. That is, it is not the same to detect text in a “controlled environment” where the text position is known and it is clearly differentiated from the rest of the image, than in a “natural environment”. In the latter, a series of factors interfere that greatly hinder the segmentation of the text, such as the noise of the camera with which the image is obtained, the poor lighting of the scene or the blurred frames that occur, for instance, if the camera is not stable.

In addition to the problems already mentioned, there is also the difficulty of locating the text within the image since it can appear in different positions and orientations. Once it has been located, each character must be carefully segmented in order to obtain a correct reading of the text.

Detection of text in an image

As mentioned above, the first challenge to face in text segmentation is the location of the text. Among the different possible methods to achieve this goal, in this case the EAST Detector will be used.

The EAST Detector is capable of detecting text practically in real time (13 fps) in both images and videos, whether in horizontal or rotated text using a convolutional neural network.

With respect to other possible algorithms, the EAST Detector has eliminated unnecessary intermediate steps so that it only has two steps. The first one is the prediction of lines of text or words using the neural network and the second one is the processing of predictions.

In the upper Figure you can see an example of the different regions with text that have been detected by the algorithm, each one marked with a green bounding box.

Decoding the text

Once the location of the text has been detected, it has to be decoded. For this, it is important to be able to isolate in the best possible way the characters from the background of the image. Therefore, different morphological operations must be applied to achieve this. These operations depend on the environment with which you are working, so the best is to evaluate each case individually to decide which ones should be applied. The Figure below presents an example of this step.

Once the text has been isolated from the background of the image, there are different methods to be able to read it. In this case, we have chosen to use the Tesseract OCR library, which is an engine for optical character recognition.

Thus, the combination of the EAST Detector together with the Tesseract library provides a fairly robust method by which the text position can be detected and read for later processing of these data.

There are different options that allow you to further refine your character recognition. For example, it is possible to indicate the language in which the text is found or if it is alphanumeric characters or exclusive numbers or letters.

Project ASIR collaboration: Sewer robots autonomous navigation

INLOC Robotics SL has been part of the Danish ASIR (Autonomous Sewer Inspection Robot) project for two years. The main objective of this project is the design of an autonomous robot, capable of navigating through the sewer and automatically detecting the defects that can be found.

Recently, during the months of November and October, a close collaboration was established with the Danish University of Aalborg (AAU). The objective was the acquisition of real data from a sewer system built specifically for this project, using a prototype robot capable of capturing 3D data.

Afterwards, by INLOC Robotics SL, the acquired data will be used for the design of localization, obstacle avoidance and sewer structural elements detection algorithms. As for the AAU, the data will be used for the final robot sensors selection.

Sewer data collection using 3D sensors:

Data collection was carried out by Ferran Plana (INLOC Robotics) and Chris H. Bahnsen (AAU) in Aarhus, Denmark. The experiments were divided into 3 parts, localization, avoidance/detection and illumination.

For the localization experiment, a series of evenly spaced perforations were made in a tube by adding cylinders into them. In such a way, while the prototype robot captures images, the cylinders will help us to create a grown truth.

The grown truth will be used to verify the effectiveness of the different localization algorithms that the final automatic sewer inspection robot will use.

To obtain data for the obstacle avoidance algorithm, typical elements were used within the sewer, roots, rocks, sand… Taking different scenes with these elements, it is expected to be able to detect these in a 3D environment, which would allow to compute the evasion strategy for the future algorithm.

Regarding the structural elements detection, a similar strategy was followed. The prototype robot was placed in positions where there were lateral connections, different types of cameras, joints… The 3D data acquisition was varied enough to prepare 3D detection algorithms.

Finally, the prototype robot has two 3D sensors of different technology, a PicoFlex (time of flight) and a RealSense (stereo image). The RealSense camera requires lighting in order to take 3D images. The question would be: how much?

Using an industrial adjustable torch, the robot was positioned in the sewer within the dark. Modifying the illumination power at fixed intervals, different images were taken in each one of them. This will make it possible to create an illumination model, which will help to choose the minimum illumination necessary for a stereo-type camera, while maintaining its precision to the maximum.

Everything is ready for the development of the first autonomous navigation versions of the final ASIR project robot. Stay tuned for updates from INLOC Robotics SL to follow the evolution of this great project! An automatic inspection robot is a highly novel product that would greatly improve the efficiency and quality of sewer system maintenance.

Object detection and inference using a point cloud obtained with a 3D camera

The detection and identification of objects and people is one of the major points of contention in the implementation of systems with a certain level of autonomy, and it is undoubtedly one of the issues with the greatest impact in the world of computer vision.

Correct detection and inference is key to be able to carry out tasks such as autonomous navigation, robot arm assistance, video surveillance or, as in our case, defect detection.

There are, fundamentally, two ways to approach object detection.

The first is based on the use of monocular cameras (normal cameras, like those of our mobiles), which provide two-dimensional images.A series of algorithms are applied to these images, which can be very varied, in order to obtain a “box” or delimitation that indicates the presence and location of the object to be detected in the image.

The second is based on the use of 3D cameras that, as the name suggests, in addition to the 2-dimensional image, add a third dimension: depth.Thanks to this we can create point clouds, like the one in Image 1. In INLOC we are using this second method to perceive the environment.

The algorithm applied to the point cloud

The algorithms that can be applied are very varied, from the simplest to the most complex.

In this post we want to show the result of detecting a cup of coffee by concatenating two very simple algorithms: first, we apply an edge detector to the original point cloud.That allows us to expose the basic geometry of most of the elements;and second, we pass it through a RANSAC algorithm, which will make an estimate of the parameters of the geometry that best adapts to the mentioned edges of the point cloud.

Image 1. The point cloud as obtained from the 3D camera. Note the cup of coffee on the table, with the wall and objects in the background as noise

Image 2. The original point cloud with the detected and located coffee cup. The original dots are in blue, the edges are pink, and the dots that fall inside the box (in red) that encompass the mug are in gray.

Which geometry to use will depend on the problem.

In the case of Image 1, the objective is to detect the cup of coffee on the table, so estimating the parameters of a circle is the most logical thing to do.

By using this method, a successful detection of the cup is achieved, as can be seen in Image 2. The good thing about this method is that, if the object to be detected is reasonably simple, the detection is not only simple but also fast.

It is not, however, robust: when there are objects with complex geometries or many instances of similarly shaped objects in the point cloud, the chances of finding unwanted objects are high.

For these cases, hybrid solutions must be explored, in which the RGB image of each candidate object is passed on to other algorithms such as convolutional neural networks, which have greater capacity for abstraction and generalization, despite being much more complex and slow.

But that’s a story for another post!

Gestión de activos eficientes para el sector del agua

Efficient asset management for the water sector

The main objective of the digital transformation of an industrial sector is to provide tools that allow the total connection between its elements so that it can act in an agile way and, even more importantly, focused on the client.This process requires a great internal effort to manage change since it completely transforms the way of working and, consequently, the mindset of all its staff.

The water sector, probably due to the high implications of responsibility regarding citizen service, is relatively conservative in this process of change, as complex as it is necessary, in the way it works and manages assets.As a consequence, it presents a low degree of digitization compared to other productive sectors.

There is a clear tendency for users to demand more information in real time, they are less tolerant of service failures and the resources available to management companies are finite and limited.Thus, it is necessary to implement monitoring systems that allow the user to obtain data -especially from a good such as water-, on the one hand, and on the other, the managers have information to carry out maintenance taskspreventive and, above all, plan it properly taking into account the needs of the network and the resources available.

Examples of digital transformation in the water sector

Although it is true that in recent years the use of technology in the management of infrastructures in the world of water has become popular, coinciding with the explosion of Smart Cities, we observe that most of the digitized processes belong to the management of thedrinking water network.We have as examples the use of digital twins, the mapping of infrastructures through GIS systems, the use of big data to offer value-added services to the user, automatic sampling points for the determination of quality … This is due, as I mentioned.previously, to the demands of the user, who is increasingly demanding of information and is increasingly aware of what he consumes.

The digital transformation has also reached the Wastewater Treatment Plants (WWTP), which have highly sensorized and digitized control panels and processes to comply with the discharge regulations, as well as meet environmental objectives through energy optimization.

However, in this process of transformation and modernization of the sector, started in the visible parts of the integral water cycle, it seems that we have forgotten about the buried infrastructure, the sewage system.This has led to the continuing occurrence of overflows or spills into the sea in times of heavy rain.

It is here where our product SEWDEF, an automatic sewer inspection analysis system, offers objective information for efficient sewage management.Inspections of the sewerage network are carried out using images recorded from TV cameras that are manually analyzed, giving rise to a report subject to the subjective perception and experience of the operator, making it difficult to temporarily trace the state of the infrastructure and study itsrate of degradation.

Sewdefis a tool for digital transformation in the water sector

The SEWDEF system is a web application processed in a cloud environment, accessible from anywhere and through any device with an Internet connection.It bases its operation on the combination of computer vision algorithms, Artificial Intelligence (Deep Learning) and mobile robotics.

In this way, infrastructure managers will be able to obtain objective information on the state of the sewerage network and its temporal evolution in order to know its rate of degradation in order to plan and prioritize preventive maintenance actions.