Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection

Abstract

Recent years have shown a noticeable rise in the number of incidents with drones, related to both civilian and military installations. While drone neutralization techniques have become increasingly effective, detection most often relies on professional equipment, which is too expensive to be used for all critical nodes and applications. Therefore, there is a need for drone detection systems that could work on low performance hardware. Its critical component consists of an object detection system. In this article, we introduce a new object detection dataset, built entirely to train computer vision based object detection machine learning algorithms for a task of binary object detection to enable automated, industrial camera based detection of multiple drone objects using camera feed. The dataset expands existing multiclass image classification and object detection datasets (ImageNet, MS-COCO, PASCAL VOC, anti-UAV) with a diversified dataset of drone images. In order to maximize the effectiveness of the model, real world footage was utilized, transformed into images and hand-labelled to create a custom set of 56821 images and 55539 bounding boxes. Additionally, semi-automated labelling was proposed, tested and proved to be very useful for object detection applications. The dataset was divided into train and test subsets for further processing and used to generate 603 easily deployable Haar Cascades as well as 819 high performing Deep Neural Networks based models. They were used to test different object detection methods to determine the long term feasibility of a large scale drone detection system utilizing machine learning algorithms. The study has shown that Haar Cascade can be used as the Minimum Viable Product model for mediocre performance but fails to scale up effectively for a larger dataset compared to the Deep Neural Network model.

An Object Detection Algorithm for Rotary-Wing UAV Based on AWin Transformer

“…This method achieves a classification accuracy of up to 85.3%. MACIEJ et al [14] proposed a new UAV image dataset and a semi-automatic labeling method for the dataset. Meanwhile, they designed a high-performance detection model based on a deep neural network.…”

DJI phantom frame quadcopter for aerial monitoring of objects

“…The direction of propeller rotation is divided into two different directions, clockwise (CW) and counterclockwise (CCW). The clockwise-rotating motors are motors C and B, while the counterclockwise-rotating motors are motors A and D. The difference in rotation of each motor aims to prevent a turning moment on the quadopter body (Arnanto et al, 2019;Pawełczyk & Wojtyra, 2020).…”

“…Meanwhile, in the latter, helicopters are usually used to monitor the disaster location. However, helicopters require a special runway for the landing site and cannot monitor optimally due to their relatively large size and vulnerability (Arnanto et al, 2019;Swamardika et al, 2014;Pawełczyk & Wojtyra, 2020).…”

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

“…Recently, the Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection [ 22 ] has been published. The dataset consists 51446 images for training and 5375 images for testing.…”