Precise Payload Delivery via Unmanned Aerial Vehicles: An Approach Using Object Detection Algorithms

TL;DR

This paper presents a novel UAV payload delivery method that uses deep learning-based object detection to improve delivery accuracy, achieving a 500% increase in horizontal precision over traditional GPS-based methods.

Contribution

The work introduces a new navigation approach combining UAVs with deep learning object detection to enhance payload delivery accuracy beyond GPS limitations.

Findings

Achieved 500% improvement in horizontal delivery precision.

Developed a micro-UAV with integrated deep learning object detection.

Demonstrated robustness under various weather conditions.

Abstract

Recent years have seen tremendous advancements in the area of autonomous payload delivery via unmanned aerial vehicles, or drones. However, most of these works involve delivering the payload at a predetermined location using its GPS coordinates. By relying on GPS coordinates for navigation, the precision of payload delivery is restricted to the accuracy of the GPS network and the availability and strength of the GPS connection, which may be severely restricted by the weather condition at the time and place of operation. In this work we describe the development of a micro-class UAV and propose a novel navigation method that improves the accuracy of conventional navigation methods by incorporating a deep-learning-based computer vision approach to identify and precisely align the UAV with a target marked at the payload delivery position. This proposed method achieves a 500% increase in average horizontal precision over conventional GPS-based approaches.