Airports Runway Monitoring System : Using Thermal Imaging Approach

Context: On airport runways, monitoring is done by Precision Runway Monitor (PRM) method with the help of radar. Most of the airports are built near the forests so there is a greater chance of mam-mal intrusion onto the runways leading to massive accidents. At many airports, there are applied old traditional, mostly manual methods in detecting mammals on the runway. Accidents caused by wildlife strikes between aircraft and mammals are increasing day to day, and this is approximately 3%-10% of all reported collisions [1]. We propose a system that monitors the airport runway by detecting mammals. Objectives: The main objective of this project is to investigate and evaluate the possibility of using thermal vision methods to detect the obstacles encountered on the runways. The system should work in real time. Methods: Mammals detection can be done by using a thermal camera with a thermal sensitivity of less than 50mK and a resolution of 640 x 480 pixels. The thermal camera uses an uncooled microbolometer sensor which is lighter, consumes less power and can see through almost all weather conditions like mist, fog, snow etc. Machine Learning based algorithms like background subtraction are used in detecting the mammal, and contours are used to estimate the size and distance. Results: As a result, the mammals moving on the runway can be detected at a distance of up to 400 m. The system estimates a distance of a moving animal and its size with an accuracy of around 90%. Conclusions: A runway monitoring system is needed to prevent wildlife strikes in airports. The proposed system prevents accidents to some extent. However, further tests are required before its commercialisation. There is a need for further quantitative and qualitative validation of the models in full-scale industry trials