Raspberry Pi Based Vision System for Foreign Object Debris (FOD) Detection

• Main Authors: Mahammad, Sarfaraz Ahmad, Sushma, Vendrapu
• Format: Others
• Language: English
• Published: Blekinge Tekniska Högskola 2020
• Subjects: Airports; Computer vision; Performance evaluation; Real-time systems; User Centered Design; Web design.; Telecommunications; Telekommunikation
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20198

Background: The main purpose of this research is to design and develop a cost-effective system for detection of Foreign Object Debris (FOD), dedicated to airports. FOD detection has been a significant problem at airports as it can cause damage to aircraft. Developing such a device to detect FOD may require complicated hardware and software structures. The proposed solution is based on a computer vision system, which comprises of flexible off the shelf components such as a Raspberry Pi and Camera Module, allowing the simplistic and efficient way to detect FOD. Methods: The solution to this research is achieved through User-centered design, which implies to design a system solution suitably and efficiently. The system solution specifications, objectives and limitations are derived from this User-centered design. The possible technologies are concluded from the required functionalities and constraints to obtain a real-time efficient FOD detection system. Results: The results are obtained using background subtraction for FOD detection and implementation of SSD (single-shot multi-box detector) model for FOD classification. The performance evaluation of the system is analysed by testing the system to detect FOD of different size for different distances. The web design is also implemented to notify the user in real-time when there is an occurrence of FOD. Conclusions: We concluded that the background subtraction and SSD model are the most suitable algorithms for the solution design with Raspberry Pi to detect FOD in a real-time system. The system performs in real-time, giving the efficiency of 84% for detecting medium-sized FOD such as persons at a distance of 75 meters and 72% efficiency for detecting large-sized FOD such as cars at a distance of 125 meters, and the average frame per second (fps) that is the system ’s performance in recording and processing frames of the area required to detect FOD is 0.95.