MAGI: Multistream Aerial Segmentation of Ground Images with Small-Scale Drones

Danilo Avola,Daniele Pannone

doi:10.3390/drones5040111

Abstract

In recent years, small-scale drones have been used in heterogeneous tasks, such as border control, precision agriculture, and search and rescue. This is mainly due to their small size that allows for easy deployment, their low cost, and their increasing computing capability. The latter aspect allows for researchers and industries to develop complex machine- and deep-learning algorithms for several challenging tasks, such as object classification, object detection, and segmentation. Focusing on segmentation, this paper proposes a novel deep-learning model for semantic segmentation. The model follows a fully convolutional multistream approach to perform segmentation on different image scales. Several streams perform convolutions by exploiting kernels of different sizes, making segmentation tasks robust to flight altitude changes. Extensive experiments were performed on the UAV Mosaicking and Change Detection (UMCD) dataset, highlighting the effectiveness of the proposed method.

Highlights

Introductionmultistream aerial segmentation of ground images (MAGI): Multistream AerialIn recent years, computer vision has been widely used in several heterogeneous tasks, including deception detection [1,2,3], background modeling [4,5,6], and person reidentification [7,8,9]
Computer vision has been widely used in several heterogeneous tasks, including deception detection [1,2,3], background modeling [4,5,6], and person reidentification [7,8,9]
Thanks to technological advancement, it is possible to execute computer-vision algorithms on different devices, including robots [10,11] and drones [12,13]. The usage of the latter has dramatically increased from both the civilian and the research world because of several factors, including lower production costs, small sizes that allow for easy transportation and deployment, and the possibility to execute complex software during flights. The latter aspect is possible by equipping the small-scale drone with single board computers (SBCs), usually separated from the main drone controller, in charge of executing software

Summary

Introduction

MAGI: Multistream AerialIn recent years, computer vision has been widely used in several heterogeneous tasks, including deception detection [1,2,3], background modeling [4,5,6], and person reidentification [7,8,9]. Thanks to technological advancement, it is possible to execute computer-vision algorithms on different devices, including robots [10,11] and drones [12,13] The usage of the latter has dramatically increased from both the civilian and the research world because of several factors, including lower production costs, small sizes that allow for easy transportation and deployment, and the possibility to execute complex software during flights. By exploiting SBCs with specific hardware for parallel computing (e.g., CUDA), it is possible to achieve real-time or near-real-time performance This is crucial for critical tasks such as fire detection [14,15,16], search and rescue [17,18,19], and environmental monitoring [20,21,22]. There are certain conditions that make both object classification or detection and segmentation algorithms difficult, such as nadir view and altitude changes during the flight

Methods

Results

Conclusion