Real-time scene classification of unmanned aerial vehicles remote sensing image based on Modified GhostNet.

ABSTRACT: Unmanned Aerial Vehicles (UAVs) play an important role in remote sensing image classification because they are capable of autonomously monitoring specific areas and analyzing images. The embedded platform and deep learning are used to classify UAV images in real-time. However, given the limited memory and computational resources, deploying deep learning networks on embedded devices and real-time analysis of ground scenes still has challenges in actual applications. To balance computational cost and classification accuracy, a novel lightweight network based on the original GhostNet is presented. The computational cost of this network is reduced by changing the number of convolutional layers. Meanwhile, the fully connected layer at the end is replaced with the fully convolutional layer. To e

SUBMITTER: Shen X 

PROVIDER: S-EPMC10246849 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature