Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV

TL;DR

This paper presents a CNN-based object detection method for UAV imagery, achieving fast and accurate results using RetinaNet on the Stanford Drone Dataset, with potential applications in surveillance and monitoring.

Contribution

It demonstrates the effectiveness of RetinaNet, a focal loss dense detector, for UAV-based object detection, highlighting its speed and accuracy improvements.

Findings

RetinaNet achieves state-of-the-art performance on UAV object detection.

The approach is both fast and accurate for real-time UAV applications.

Effective detection results on Stanford Drone Dataset.

Abstract

Unmanned Aerial Vehicles (UAVs), have intrigued different people from all walks of life, because of their pervasive computing capabilities. UAV equipped with vision techniques, could be leveraged to establish navigation autonomous control for UAV itself. Also, object detection from UAV could be used to broaden the utilization of drone to provide ubiquitous surveillance and monitoring services towards military operation, urban administration and agriculture management. As the data-driven technologies evolved, machine learning algorithm, especially the deep learning approach has been intensively utilized to solve different traditional computer vision research problems. Modern Convolutional Neural Networks based object detectors could be divided into two major categories: one-stage object detector and two-stage object detector. In this study, we utilize some representative CNN based object detectors to execute the computer vision task over Stanford Drone Dataset (SDD). State-of-the-art performance has been achieved in utilizing focal loss dense detector RetinaNet based approach for object detection from UAV in a fast and accurate manner.