Phase image-guided adaptive rotation-invariant feature point detector

ABSTRACT

Vision-based autonomous obstacle avoidance for unmanned air vehicle (UAV) is a vital research field to ensure the safety of UAV flight in the national airspace. In this paper, a proposed detector is introduced to extract the feature (corner) points of static obstacles from the surrounding cluttered environment for small quadrotor UAVs flying at low altitudes. The detected feature points are the bottleneck for most autonomous obstacle avoidance to facilitate tracking and identification of risk processes during UAV's take-off phase. The proposed feature point detector is based on the estimated phase image of the first image derivatives and the homogeneity test examination to give the proposed detector adaptive behavior. The performance of the proposed detector is compared with various feature point detectors in terms of several quantitative assessment indices and environmental conditions. From the experimental results, the proposed detector achieves promising results for significantly extracting the surrounding feature points.

KEYWORDS:

• Corner point detection
• phase image
• homogeneity test
• cluttered environment
• autonomous obstacle avoidance
• vision-based navigation
• unmanned air vehicle (UAV)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ahmed S. Mashaly

Ahmed S. Mashaly received the B.Sc. in Electrical Engineering in 2004 and the M.Sc. degree in Electrical Engineering in 2011, from the Military Technical College, Cairo, Egypt. He received his Ph.D. in Electrical Engineering from Beijing University of Aeronautics and Astronautics, Beijing, China, in 2017. In 2018, he joined to the Department of Airborne Equipment of Military Technical College (MTC) of Egypt as a Lecturer. His research interests are mainly focused on vision and imaging systems, image processing, image analysis, pattern recognition, vision-based navigation, dimensionality reduction techniques, and their applications in machine vision.

Tarek A. Mahmoud

Tarek A. Mahmoud was born in Cairo, Egypt, in 1972. He received the B.Sc. and M.Sc. degrees in Electrical Engineering from Military Technical College, Cairo, Egypt, in 1994 and 2000, respectively. He received a Ph.D. degree in Electrical Engineering from the University of Strathclyde, Glasgow, U.K., in 2008. From 2008 until now, he is with the Computer Engineering Department, Military Technical College. His current research interests include image processing, image enhancement, super-resolution, pattern recognition, and biometric authentication.