Review on Real-Time Drone Detection Based on Visual Band Electro-Optical (EO) Sensor

Drones have gained a lot of traction in the fields of amateur and professional video creation. They are con- strained by more than just civilian lines of action. Drones, on the other hand, have been employed in terrorist attacks, privacy breaches, and inadvertent accidents in high-risk areas like air- ports. This is a major source of risk for important sites. It is not easy to identify such attacks in a timely manner. Many researchers from various fields are looking into this issue. New technologies have been devised to manage and monitor the horizon in reaction to these incidents, ensuring protection in high-risk locations. From a cost-to-quality aspect, the use of camera systems for drone detection is extremely appealing. Drone detection via visual information, on the other hand, is impeded by drones' striking resemblance to other things, such as birds or aircraft. Furthermore, because drones could travel at high speeds, detection is preferred in the real-time domain. This paper goes into making a review on current algorithms for drone detection in real-time domain based on EO sensors. The most typical methodology for drone detection is classified to some traditional computer vision methods and recently deep learning methods. This paper aims to guide the researchers to design upcoming drone detecting systems using the most practical technologies that satisfy certain requirements such as adaptability, extensibility, dependability, and affordability.