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Digital towers using high-resolution cameras that cover a 360-degree view of airports have recently been applied as a solution for some airports by replacing conventional towers. Although many computer vision systems have been developed as tools to assist tower controllers, small flying object detection remains challenging due to their small dimensions and unpredictable trajectories. This paper proposes a novel computer vision framework to detect, track and recognize small flying objects, namely aircraft and drones, in an airport environment. The framework creates a new Convolutional Neural Network which adapts to the unique characteristics of small flying objects. It also exploits the spatial-temporal information, as well as post-processing, to improve the performance. The proposed framework is validated on an airport dataset and Drone-vs-Bird public dataset. The results show that the framework can not only perform object detection in real-time, but also surpass the performance of state-of-the-art models in both datasets by a large margin.

Airport runway and taxiway (airside) areas are highly complex environments, where safe-separation procedures must be maintained by Air Traffic Controllers (ATCs) under varying visibility and traffic conditions. In this paper, we propose a novel computer-vision based framework, namely Deep4Air, for automated visual monitoring of airside operations, providing real-time data including aircraft location, speed, and distance analytics. This framework includes an adaptive deep neural network that exploits a depth-wise convolutional operator for efficient detection and tracking of aircraft. The experimental results show an average precision of detection and tracking of up to 98.2% on simulated data with validation on surveillance videos from the digital tower at George Bush Intercontinental Airport. The results also demonstrate that Deep4Air can locate aircraft positions relative to the airport runway and taxiway infrastructure with high accuracy. Furthermore, aircraft speed and separation distance are monitored in real-time, providing enhanced safety management.

Unmanned aerial vehicles (UAVs) have brought many practical benefits during the last decades. Moreover, as technology advances, UAVs become more optimal in size and range. However, the threat posed by these devices is also increasing if people misuse them for illegal activities (such as terrorism, drug trafficking, etc.), which poses a high risk to security for different organizations and governments. Hence, detection and monitoring of drones are crucial to prevent security breaches. However, the small size and similarity to wild birds in the complex background of drones pose a significant challenge. This paper addresses the detection of small drones in real surveillance videos using standard deep learning-based object recognition methods. Our method approaches the drone detection problem by training the YOLOv4 model with modifications in the network structure, training strategy, and pre-anchor boxes for better small object detection. We also integrate the Seq-NMS post-processing phase to increase detection reliability and reduce false alarms. The experimental results show that our approach can perform better than the previous methods.