DeepAI AI Chat
Log In Sign Up

Conifer Seedling Detection in UAV-Imagery with RGB-Depth Information

by   Jason Jooste, et al.

Monitoring of reforestation is currently being considerably streamlined through the use of drones and image recognition algorithms, which have already proven to be effective on colour imagery. In addition to colour imagery, elevation data is often also available. The primary aim of this work was to improve the performance of the faster-RCNN object detection algorithm by integrating this height information, which showed itself to notably improve performance. Interestingly, the structure of the network played a key role, with direct addition of the height information as a fourth image channel showing no improvement, while integration after the backbone network and before the region proposal network led to marked improvements. This effect persisted with very long training regimes. Increasing the resolution of this height information also showed little effect.


page 2

page 10


Combining geolocation and height estimation of objects from street level imagery

We propose a pipeline for combined multi-class object geolocation and he...

Single-View Height Estimation with Conditional Diffusion Probabilistic Models

Digital Surface Models (DSM) offer a wealth of height information for un...

KL-Divergence-Based Region Proposal Network for Object Detection

The learning of the region proposal in object detection using the deep n...

GiraffeDet: A Heavy-Neck Paradigm for Object Detection

In conventional object detection frameworks, a backbone body inherited f...

Overhead Detection: Beyond 8-bits and RGB

This study uses the challenging and publicly available SpaceNet dataset ...

Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning

Plastic shopping bags that get carried away from the side of roads and t...