UAV Landing Using Computer Vision Techniques for Human Detection

The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed—without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5–10 m, with recalls from 59%–76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

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Real time Implementation of Face Recognition System on Raspberry Pi

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.17.11564 ◽

2018 ◽

Vol 7 (2.17) ◽

pp. 85

Author(s):

K Raju ◽

Dr Y.Srinivasa Rao

Keyword(s):

Face Recognition ◽

High Performance ◽

Recognition Rate ◽

Cost Effective ◽

Recognition System ◽

Raspberry Pi ◽

Face Recognition System ◽

Effective System ◽

Face Detection And Recognition ◽

Improved Accuracy

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance.

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Vision based Office Automation and Security System using Machine Learning and Internet of Things

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.24.11996 ◽

2018 ◽

Vol 7 (2.24) ◽

pp. 42

Author(s):

Amber Goel ◽

Apaar Khurana ◽

Pranav Sehgal ◽

K Suganthi

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Face Recognition ◽

Internet Of Things ◽

Office Automation ◽

Detection Algorithm ◽

Machine Learning Algorithms ◽

Human Detection ◽

Raspberry Pi ◽

Efficient Tool

The paper focuses on two areas, automation and security. Raspberry Pi is the heart of the project and it is fuelled by Machine Learning Algorithms using Open CV and Internet of Things. Face recognition uses Linear Binary Pattern and if an unknown person uses their workstation, a message will be sent to the respective person with the photo of the person who uses the workstation. Face recognition is also being used for uploading attendance and switching ON and OFF appliances automatically. During un-official hours, A Human Detection algorithm is being used to detect the human presence. If an unknown person enters the office, a photo of the person will be taken and sent to the authorities. This technology is a combination of Computer Vision, Machine learning and Internet of things, that serves to be an efficient tool for both automation and security.

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Building a Raspberry Pi School Magnetometer Network in the UK

10.5194/gc-2018-10 ◽

2018 ◽

Author(s):

Ciarán D. Beggan ◽

Steve R. Marple

Keyword(s):

Magnetic Field ◽

Geomagnetic Storms ◽

Low Cost ◽

Cost Effective ◽

Raspberry Pi ◽

Data Logger ◽

Weather Events ◽

Effective System ◽

The Uk ◽

Magnetic Field Variations

Abstract. As computing and geophysical sensor components have become increasingly affordable over the past decade, it is now possible to design and build a cost-effective system for monitoring the Earth's natural magnetic field variations, in particular for space weather events. Modern fluxgate magnetometers are sensitive down to the sub-nanotesla level, which far exceeds the level of accuracy required to detect very small variations of the external magnetic field. When the popular Raspberry Pi single-board computer is combined with a suitable digitiser it can be used as a low-cost data logger. We adapted off-the-shelf components to design a magnetometer system for schools and developed bespoke Python software to build a network of low-cost magnetometers across the UK. We describe the system and software and how it was deployed to schools around the UK. In addition, we show the results recorded by the systems from one of the largest geomagnetic storms of the current solar cycle.

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Building a Raspberry Pi school magnetometer network in the UK

Geoscience Communication ◽

10.5194/gc-1-25-2018 ◽

2018 ◽

Vol 1 (1) ◽

pp. 25-34 ◽

Cited By ~ 1

Author(s):

Ciarán D. Beggan ◽

Steve R. Marple

Keyword(s):

Magnetic Field ◽

Geomagnetic Storms ◽

Low Cost ◽

Cost Effective ◽

Raspberry Pi ◽

Data Logger ◽

Weather Events ◽

Effective System ◽

The Uk ◽

Magnetic Field Variations

Abstract. As computing and geophysical sensor components have become increasingly affordable over the past decade, it is now possible to design and build a cost-effective system for monitoring the Earth's natural magnetic field variations, in particular for space weather events. Modern fluxgate magnetometers are sensitive down to the sub-nanotesla (nT) level, which far exceeds the level of accuracy required to detect very small variations of the external magnetic field. When the popular Raspberry Pi single-board computer is combined with a suitable digitiser it can be used as a low-cost data logger. We adapted off-the-shelf components to design a magnetometer system for schools and developed bespoke Python software to build a network of low-cost magnetometers across the UK. We describe the system and software and how it was deployed to schools around the UK. In addition, we show the results recorded by the system from one of the largest geomagnetic storms of the current solar cycle.

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Extended Routine Follow-up of Primary Uncomplicated Hip and Knee Arthroplasty is not Necessary

The Open Orthopaedics Journal ◽

10.2174/1874325001913010266 ◽

2019 ◽

Vol 13 (1) ◽

pp. 266-271

Author(s):

Georgina Kakra Wartemberg ◽

Thomas Goff ◽

Simon Jones ◽

James Newman

Keyword(s):

Aseptic Loosening ◽

Revision Surgery ◽

Revision Arthroplasty ◽

Cost Effective ◽

Face To Face ◽

Primary Surgery ◽

Effective System ◽

Knee Revision Surgery ◽

Knee Revision

Aims: To create a more effective system to identify patients in need of revision surgery. Background: There are over 160,000 total hip and knee replacements performed per year in England and Wales. Currently, most trusts review patients for up to 10 years or more. When we consider the cost of prolonged reviews, we cannot justify the expenditure within a limited budget. Study Design & Methods: We reviewed all patients' notes that underwent primary hip and knee revision surgery at our institution, noting age, gender, symptoms at presentation, referral source, details of the surgery, reason for revision and follow up history from primary surgery. Results: There were 145 revision arthroplasties (60 THR and 85 TKR) that met our inclusion criteria. Within the hip arthroplasty group, indications for revision included aseptic loosening (37), dislocation (10), and infection (3), periprosthetic fracture, acetabular liner wear and implant failure. All thirty-seven patients with aseptic loosening presented with pain. Twenty-five were referred from general practice with new symptoms. The remaining were clinic follow-ups. The most common reason for knee revision was aseptic loosening (37), followed by infection (21) and then progressive osteoarthritis (8). Most were referred from GP as a new referral or were clinic follow-ups. All patients were symptomatic. Conclusion: All the patients that underwent revision arthroplasty were symptomatic. Rather than yearly follow up, we recommend a cost-effective system. We are implementing a 'non face-to-face' system. Patients would be directly sent a questionnaire and x-ray form. The radiographs and forms will be reviewed by an experienced arthroplasty surgeon. The concerning cases will be seen urgently in a face-to-face clinic.

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Developing Algorithms for a Berry Recognition System Used in Robotized Harvesting of Garden Strawberry

Elektrotekhnologii i elektrooborudovanie v APK ◽

10.22314/2658-4859-2020-67-1-133-141 ◽

2020 ◽

Vol 67 (1) ◽

pp. 133-141

Author(s):

Dmitriy O. Khort ◽

Aleksei I. Kutyrev ◽

Igor G. Smirnov ◽

Rostislav A. Filippov ◽

Roman V. Vershinin

Keyword(s):

Computer Vision ◽

Technological Process ◽

Degrees Of Freedom ◽

Computer Control ◽

Recognition System ◽

Raspberry Pi ◽

Laser Sensors ◽

Lighting Conditions ◽

Systems Research ◽

Degree Of Maturity

Technological capabilities of agricultural units cannot be optimally used without extensive automation of production processes and the use of advanced computer control systems. (Research purpose) To develop an algorithm for recognizing the coordinates of the location and ripeness of garden strawberries in different lighting conditions and describe the technological process of its harvesting in field conditions using a robotic actuator mounted on a self-propelled platform. (Materials and methods) The authors have developed a self-propelled platform with an automatic actuator for harvesting garden strawberry, which includes an actuator with six degrees of freedom, a co-axial gripper, mg966r servos, a PCA9685 controller, a Logitech HD C270 computer vision camera, a single-board Raspberry Pi 3 Model B+ computer, VL53L0X laser sensors, a SZBK07 300W voltage regulator, a Hubsan X4 Pro H109S Li-polymer battery. (Results and discussion) Using the Python programming language 3.7.2, the authors have developed a control algorithm for the automatic actuator, including operations to determine the X and Y coordinates of berries, their degree of maturity, as well as to calculate the distance to berries. It has been found that the effectiveness of detecting berries, their area and boundaries with a camera and the OpenCV library at the illumination of 300 Lux reaches 94.6 percent’s. With an increase in the robotic platform speed to 1.5 kilometre per hour and at the illumination of 300 Lux, the average area of the recognized berries decreased by 9 percent’s to 95.1 square centimeter, at the illumination of 200 Lux, the area of recognized berries decreased by 17.8 percent’s to 88 square centimeter, and at the illumination of 100 Lux, the area of recognized berries decreased by 36.4 percent’s to 76 square centimeter as compared to the real area of berries. (Conclusions) The authors have provided rationale for the technological process and developed an algorithm for harvesting garden strawberry using a robotic actuator mounted on a self-propelled platform. It has been proved that lighting conditions have a significant impact on the determination of the area, boundaries and ripeness of berries using a computer vision camera.

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