Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge

Abstract

Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.

Performance Enhancement of Optimized Link State Routing Protocol by Parameter Configuration for UANET

“…The growing number of crimes has highlighted the significance of identifying and disabling unlawfully deployed drones. In [10], the authors discuss techniques for identifying drones in the presence of flying objects such as birds. In [11], the authors propose a deep learning method for detecting malicious drones using image and audio data.…”

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

“…In this work, the standard Cascade R-CNN architecture, Faster R-CNN, YOLOv3 network, and YOLOv5 network were used to identify drones vs. birds. The discussion on detection in a variety of backgrounds with additional data also needs to be extended [53].…”

Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

“…Traditional image processing algorithms have a fast computation speed and low resource consumption. However, it is easy to obtain a lower detection accuracy when dealing with insulator defect areas, because similar information will interfere with them in the background [3,4].…”