AI Based Drone Threat Detection and Alert System

The rapid increase in the use of drones or Unmanned Aerial Vehicles (UAVs) for both commercial and recreational purposes has introduced new challenges related to security, privacy, and safety. Drones can be misused for illegal activities such as smuggling, spying, and even weaponization, creating a critical need for efficient, cost- effective detection systems. Traditional drone detection methods, like radar-based systems, are often costly, complex, and inaccessible for smaller scale deployments This project proposes a low- cost, AI- based drone detection system built using a Raspberry Pi and camera module, offering an affordable and scalable solution for monitoring unauthorized drones in various environments. The core idea behind this project is to use computer vision techniques, combined with machine learning models, to detect drones in real time through image feeds captured by the camera. The system employs image recognition algorithms, particularly convolutional neural networks (CNNs), to distinguish drones from other flying objects or environmental noise. Raspberry Pi serves as the primary processing unit, running the detection model and providing real- time alerts when drones are detected. The use of a Raspberry Pi is advantageous due to its small size, low cost, and ability to process real- time data, making it suitable for a wide range of applications, including personal and commercial use.

Authors

Publication Details

Journal
International Journal of Innovative Research in Engineering
Published
2025-05-14
DOI
https://doi.org/10.59256/ijire.20250603006
Primary Topic
Fire Detection and Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Based Drone Threat Detection and Alert System

C N Srividya, B. Sai Chandana, S. Ramya, Manoj Kumar S B et al.
International Journal of Innovative Research in Engineering
Fire Detection and Safety Systems
article

AI Based Drone Threat Detection and Alert System

C N Srividya, B. Sai Chandana, S. Ramya, Manoj Kumar S B, Mohan Kumar. B, S L Vidyamani
article en

Abstract

The rapid increase in the use of drones or Unmanned Aerial Vehicles (UAVs) for both commercial and recreational purposes has introduced new challenges related to security, privacy, and safety. Drones can be misused for illegal activities such as smuggling, spying, and even weaponization, creating a critical need for efficient, cost- effective detection systems. Traditional drone detection methods, like radar-based systems, are often costly, complex, and inaccessible for smaller scale deployments This project proposes a low- cost, AI- based drone detection system built using a Raspberry Pi and camera module, offering an affordable and scalable solution for monitoring unauthorized drones in various environments. The core idea behind this project is to use computer vision techniques, combined with machine learning models, to detect drones in real time through image feeds captured by the camera. The system employs image recognition algorithms, particularly convolutional neural networks (CNNs), to distinguish drones from other flying objects or environmental noise. Raspberry Pi serves as the primary processing unit, running the detection model and providing real- time alerts when drones are detected. The use of a Raspberry Pi is advantageous due to its small size, low cost, and ability to process real- time data, making it suitable for a wide range of applications, including personal and commercial use.

International Journal of Innovative Research in Engineering
Openalex Percentile: Top 88%
Fire Detection and Safety Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.