This paper addressed an UAV-based solution for forest inspection from the aspect of data processing. Insect infected trees can be identified from aerial imagery. The forest inspection data processing divided into two main phases, which are tree detection and tree localization. For the tree detection task, the Deep Learning-based approach has been used. In this study, a conventional and active learning method have been used for the training. In order to map the detected trees from the video frame to geographical locations, a custom geo-localization algorithm has been developed. The proposed system loads the inspection video as an input and calculates and outputs the detected trees’ GPS coordinates. In the calculation Digital Elevation Model and camera parameters are used in addition to drone geo-data to calculate the tree positions.
The applications of unmanned aerial systems have dramatically increased in the late days, in particular within dull, dirty and dangerous missions, where it has been clearly shown that unmanned systems are better suited than manned aircrafts. The reduction of human involvements leads to an increasing demand for autonomous and operationally adaptive airborne systems. In addition, unmanned aerial systems share similar limitations with most computer embedded systems, e.g., limited space and power resources, and increasing computation requirements and complexity of the applications running onboard. To fulfill these requirements and achieve the fundamental features of autonomy and adaptability, the AREIOM—Adaptive Research Multicopter Platform—along with its layered modular architecture and the specific details of its underlying hardware and software components are presented.
This paper describes a finite state machine for adaptive mission control of mini aerial vehicles. The purpose of a finite state machine is to support mission control during aerial inspection of high voltage transmission lines and insulators independently from environmental and other conditions. One of the basic application of our mini aerial vehicle research is inspection of high voltage transmission lines during its load mode. Around high voltage transmission lines and towers generating a strong electromagnetic field. An electromagnetic fields can be influence negatively to autonomous flight and mission control should be predicted such us situation to avoid possible accident. Moreover environmental and weather condition always unpredictable and mission control have to adapt for various type of additional constrains which can be make problem for pre-defined mission map and trajectory. Therefore, it needs to develop mission control for autonomous flight with an adaptive option or capability. Such mission control provides less workload and safety guarantee for the inspection team during the process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.