Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 5 | Issue - 1 | march 2023
Published
28 February, 2023
A major portion of the earth remains unexplored today. There are many reasons for this. One of them is because those places are or maybe dangerous for the explorers. Also, Nepal and many other under-developed countries buy satellite images and maps of their territory from other countries. The problem with these maps is that they are not always up-to-date and are very low-resolution images. In order to solve these problems, this research work has designed a quadcopter (four propellers) drone with HD camera. This device is capable of capturing images with colour, intensity, and depth values at each pixel so that a 3D map can be constructed. Unmanned Aerial Vehicles (UAVs) also called Drones can be designed for numerous objectives and tasks. This article intends to design a drone capable of creating its own pathway and taking flight autonomously. During the flight, the UAV is programmed to capture high-resolution images at regular intervals with 60-80% overlap. The overlap between images is highly necessary to obtain numerous data of the same point, which will be beneficial in creating a finer 3D map. The 3D map is generated by using a process called photogrammetry, which takes pictures as input and displays a 3D map as output by utilizing the data present in pictures. This drone can be used to capture pictures and create 3D maps of unexplored areas like caves, volcano craters, etc. Also, high-resolution pictures can be merged and uploaded for satellite imaging applications. Besides, the same vehicle can be used for many other applications like forest fire monitoring systems, endangered wildlife monitoring, surveying, and so on with slight alterations.
KeywordsDrones UAV Photogrammetry Propellers Pixels QuadCopter Digital Elevation Model (DEM)
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