International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 12 | Dec 2024
p-ISSN: 2395-0072
www.irjet.net
BLIZZARD DETECTION – ML ENABLED SENSOR SYSTEM FOR OBJECT DETECTION IN SNOWFALL Dr. A. Jeyamurugan1, Asmitha K2, Kamali M3, Divya Sri S4 1Professor, Dr. A. Jeyamurugan , Department of Computer Science & Engineering, Paavai Engineering College,
Namakkal,Tamilnadu, India 2Student, Asmitha K, Department of Computer Science & Engineering, Paavai Engineering
College, Namakkal, Tamilnadu, India. 3Student Kamali M, Department of Computer Science & Engineering, Paavai Engineering College, Namakkal, Tamilnadu, India. 4Student, Divya Sri S, Department of Computer Science & Engineering, Paavai Engineering College, Namakkal, Tamilnadu, India. -------------------------------------------------------------------***-------------------------------------------------------------------Abstract - This project presents a drone-based detection system designed to assist in rescue operations in cold regions by identifying people or animals in distress. Utilizing an object detection algorithm, the drone camera detects individuals or animals and transmits data to a ground-based kit through serial communication. The kit, equipped with an LDR, temperature sensor, GPS, ultrasonic sensor, Arduino Uno, LCD display, and IoT module, monitors key environmental conditions. If a person is detected, the system relays their GPS location to an application, aiding in rapid rescue response. The ultrasonic sensor calculates the distance between the drone and detected object, while the temperature sensor and LDR provide additional environmental data, all of which are displayed on an LCD and accessible via IoT on the app. The application features two interfaces: the first shows the drone's ID and operational status, and the second displays real-time data. This system offers a robust solution for remote rescue monitoring, enhancing situational awareness and response efficiency in challenging environments. Key Words: Internet of Things, Machine Learning, Numpy, Global Positioning System, Internet of Things Module , Third Party Hardware , Serial Monitor.
1. INTRODUCTION This project presents a drone-based detection system designed to address the challenges of extreme cold environments by assisting in the timely location of individuals or animals in distress. Using advanced object detection algorithms, the drone captures real-time footage, identifies subjects in need, and seamlessly communicates data through serial transmission to a ground kit for enhanced monitoring. The ground kit, equipped with an LDR, temperature sensor, GPS module, ultrasonic sensor, Arduino Uno, LCD display, and IoT functionality, collects vital environmental data and pinpoints the GPS location of detected subjects. This information, including distance measurements, temperature, and light intensity, is displayed on an LCD and transmitted to a mobile application with dual interfaces for improved situational awareness. By integrating aerial surveillance with ground-based systems, this innovative solution empowers rescue teams with accurate, real-time data, ensuring efficient and swift responses in remote, harsh conditions.
2. LITERATURE SURVEY A literature survey underscores the advancements and challenges in drone-assisted rescue operations, particularly in cold and remote regions. Research highlights the growing integration of drones in search-and-rescue (SAR) missions due to their ability to access hard-to-reach areas with agility and precision. Object detection algorithms such as YOLO and Faster R-CNN have demonstrated their effectiveness in identifying individuals or animals in distress in real-time scenarios. The implementation of sensors like GPS modules, ultrasonic sensors, and temperature sensors has further enhanced the functionality of these systems by enabling accurate location tracking, environmental monitoring, and proximity detection. IoT-based solutions play a crucial role in improving response times by facilitating seamless data transmission between drones, ground systems, and mobile applications. However, challenges persist in optimizing performance under extreme weather conditions, integrating diverse sensors, and ensuring energy efficiency, which highlights the importance of developing advanced drone-based systems tailored for rescue missions in such environments.
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