Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed.

In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty.

Aviation reports indicate that between the years of 1988 and 2019 there were 292 human fatalities and 327 injuries that had been reported due to wildlife strikes with airplanes. To minimize these numbers a new approach to airport Wildlife Hazard Management (WHM) is presented in the following article. The proposed solution is based on the data fusion of thermal and vision streams which are used to improve the reliability and adaptability of the real-time WHM system. The system is designed to operate in all environmental conditions and provides an advance information of the fauna presence at the airport's runway. The proposed sensor fusion approach was designed and developed using user driven design methodology. Moreover, the developed system has been validated in real case scenarios and previously installed at an airport. Performed tests proved detection capabilities during day and night of dog sized animals up to 300 meters. Moreover, by using machine learning algorithms during daylight the system was able to classify person sized objects with over 90% efficiency up to 300 meters and dog sized objects up to 200 meters. The overall threat level accuracy based on the three safety zones, was 94%.