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

08/16/2018
by   Xiaoliang Wang, et al.
0

The ever-growing interest witnessed in the acquisition and development of unmanned aerial vehicles (UAVs), commonly known as drones in the past few years, has brought generation of a very promising and effective technology. Because of their characteristic of small size and fast deployment, UAVs have shown their effectiveness in collecting data over unreachable areas and restricted coverage zones. Moreover, their flexible-defined capacity enables them to collect information with a very high level of detail, leading to high resolution images. UAVs mainly served in military scenario. However, in the last decade, they have being broadly adopted in civilian applications as well. The task of aerial surveillance and situation awareness is usually completed by integrating intelligence, surveillance, observation, and navigation systems, all interacting in the same operational framework. To build this capability, UAV's are well suited tools that can be equipped with a wide variety of sensors, such as cameras or radars. Deep learning has been widely recognized as a prominent approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; however, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for UAV based object detection. State-of-the-art performance result has been showed on the UAV captured image dataset-Stanford Drone Dataset (SDD).

READ FULL TEXT

page 1

page 4

research
03/03/2018

Focal Loss Dense Detector for Vehicle Surveillance

Deep learning has been widely recognized as a promising approach in diff...
research
12/23/2020

SyNet: An Ensemble Network for Object Detection in UAV Images

Recent advances in camera equipped drone applications and their widespre...
research
09/16/2020

Perceiving Traffic from Aerial Images

Drones or UAVs, equipped with different sensors, have been deployed in m...
research
11/30/2022

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

Unmanned air vehicles (UAVs) popularity is on the rise as it enables the...
research
07/25/2019

SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer...
research
04/25/2019

Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation

It is important to find the target as soon as possible for search and re...
research
11/23/2016

Object Detection using Image Processing

An Unmanned Ariel vehicle (UAV) has greater importance in the army for b...

Please sign up or login with your details

Forgot password? Click here to reset