Heterogeneous parallelization for object detection and tracking in UAVs.

Rabah, Mohammed; Rohan, Ali; Haghbayan, Mohammad-Hashem; Plosila, Juha; Kim, Sung-Ho

Abstract

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottle-neck of computing AI applications on UAV's resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.

Citation

RABAH, M., ROHAN, A., HAGHBAYAN, M.-H., PLOSILA, J. and KIM, S.-H. 2020. Heterogeneous parallelization for object detection and tracking in UAVs. IEEE access [online], 8, pages 42784-42793. Available from: https://doi.org/10.1109/ACCESS.2020.2977120

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2020
• Online Publication Date: Feb 28, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Jul 18, 2023
• Publicly Available Date: Jul 18, 2023
• Journal: IEEE access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 42784-42793
• DOI: https://doi.org/10.1109/ACCESS.2020.2977120
• Keywords: CNN; Object detection; Object tracking; Grain-scheduled PID; Quadcopter
• Public URL: https://rgu-repository.worktribe.com/output/1982290