Smart Accident Detection and Alert System

Abstract

This paper presents an IoT-based real-time accident detection and alert system using Raspberry Pi. The system utilizes an accelerometertodetectsuddenimpacts,analcoholsensortomonitorthedriver\'scondition,andacameramoduletocapture the driver\'s image upon accident detection. The geographical location is retrieved using the IPStack API instead of a traditional GPS module. A machine learning model processes real-time video input to identify accidents visually. When an accidentisdetectedbyeithertheaccelerometerortheMLmodel,relevantdataincludingdriverimages,accidentframes,and locationare sentviaemailusingSMTPprotocol.Additionally,allsystemdataisuploadedandvisualizedinreal-timeonthe ThingSpeak cloud platform, offering graphical insights for monitoring. This integrated solution enhances post-accident response time, driver condition analysis, and centralized cloud-based monitoring.

Introduction

This project introduces an IoT-enabled accident detection and alert system leveraging Raspberry Pi, aiming to reduce response times and enhance road safety. It integrates multiple technologies, including sensors, machine learning, cloud services, and email communication to provide a dual-layer accident detection system.

Key Features:

1. Sensor Integration:

   • Accelerometer (MPU6050): Detects sudden acceleration or impact.

   • Alcohol Sensor (MQ-3): Detects driver intoxication.

   • Camera Module: Captures driver's image and streams video for ML analysis.

2. Machine Learning-Based Detection:

   • Uses a CNN-based model to analyze real-time video and detect accident frames.

3. Geolocation:

   • Replaces GPS with the IPStack API to retrieve latitude and longitude via IP address, reducing hardware complexity and cost.

4. Alert System:

   • Email Notifications via SMTP include:

     • Driver’s image

     • Accident video frame

     • Location data

     • Alcohol test result

5. Cloud Monitoring (ThingSpeak):

   • Real-time sensor data is uploaded for graphical visualization, enabling remote tracking and historical data analysis.

System Workflow:

1. Initialization: Raspberry Pi initializes and begins collecting data from sensors and video feed.

2. Dual Detection:

   • Sensor-Based: Collision and alcohol levels.

   • Vision-Based: Accident detection from video.

3. Event Trigger:

   • Triggers alerts, captures images, retrieves location.

4. Cloud Logging: Data is sent to ThingSpeak for visualization and analysis.

5. Email Alert: All critical data is compiled and sent to predefined contacts.

Hardware & Software Implementation:

• Hardware: Raspberry Pi 4, MPU6050, MQ-3, Camera.

• Software: Python scripts, OpenCV, IPStack API, SMTP protocol, and ThingSpeak cloud.

• Performance:

  • ML model achieved >90% accuracy in accident detection.

  • Email alerts sent within 5–8 seconds.

  • Reliable cloud-based visualization with real-time data updates.

Comparative Literature Survey:

The proposed system outperforms earlier models (which used only GPS, GSM, or alcohol sensors) by integrating ML, real-time video, and cloud-based monitoring, offering a more intelligent, responsive, and comprehensive solution.

Conclusion

This project presents a comprehensive and efficient accident detectionandalertingsystem designedusing RaspberryPiand various sensors integrated through IoT. The primary objective wastoensurequickidentificationandnotificationincaseofroad accidents, thereby reducing emergency response time and potentially saving lives. The system isbuiltaround a Raspberry Pi, which serves as the centralcontrollerandprocessesinputsfrommultiplehardware components.Anaccelerometersensordetectssuddenmotionor collisiontodeterminewhetheranaccidenthasoccurred.Insuch cases, a camera module captures the driver’s image at the moment of impact, providing a clear record of the driver’s condition.Analcoholsensorchecksforthepresenceofalcohol inthedriver\'sbreath,therebycontributingtoresponsibledriving practices. In addition to sensor-based detection, a video stream is continuouslyanalyzedusingamachinelearningmodeltrainedto recognizeaccidentframes.Ifanaccidentisdetectedinthevideo input,thecorrespondingframeiscapturedandstored.Thisdual- layerdetectionsystem—viabothsensorandML—ensureshigh reliability and reduces the risk of false positives or missed accidents. Instead of a conventional GPS module, the system uses the IPStack APIto determine the geographicallocation (latitude and longitude) of the device when an accident is detected. This reducestheneedforextrahardwarewhilestillensuringlocation awareness. All gathered data—including the accident frame, driver\'s image, and current location—is compiled and sent via emailusingSMTPprotocolstopredefinedemergencycontacts or control centers. To ensure long-term monitoring and analytical evaluation, all sensor data is transmitted to the ThingSpeak IoT platform, whereitisstoredandvisualizedinreal-timeusinggraphical charts. This facilitates effective post-event analysis, system behavior monitoring, and future decision-making based on trends and patterns. The system has been tested under various scenarios and has shown promising results in terms of accuracy, efficiency, and reliability.Itoffersalow-cost,scalable,andeffectivesolutionfor real-timeaccidentdetectioninsmarttransportationsystems.The overall design reduces hardware dependency while increasing functionality through software integration, making it ideal for deployment in developing regions with limited resources. Inconclusion,the proposedsystemaddressesacritical need in intelligent transport and road safety by combining hardware innovation, machine learning, IoT, and cloud connectivity. It stands as a practical and impactful contribution toward minimizing accident-related fatalities and ensuring safer road usage.

References

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Copyright

Copyright © 2025 Mrs. Chetali Mhetre, Yash Jadhav, Tanmay Karangutkar, Rushikesh Wakchaure. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.