International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
A Study and Analysis of Real Time Crash Detection: Implementation of Rapid Response Crash Detection System using Web Application Mayur Padule1, Prasad Choudhary 2, Shreyas Gune3, Ashish Mare4, Prof. Rohini Tarade5 5Professor, Dept of B.Tech Computer Engineering, Ajeenkya D Y Patil University (ADYPU), Pune, Maharashtra,
India 1,2,3,4 B.Tech in Computer Engineering, Dept. of B.tech Computer Engineering, Ajeenkya D Y Patil University
(ADYPU), Pune, Maharashtra, India. -----------------------------------------------------------***---------------------------------------------------------Abstract - This paper introduces a Python-based accident detection software system aimed at enhancing emergency response to road accidents. Leveraging sensor data and machine learning algorithms, the system autonomously identifies accident events in real-time and promptly sends emergency signals with precise location information to relevant authorities. Through rigorous testing and validation, the system's effectiveness and reliability are demonstrated, highlighting its potential to significantly reduce response times and improve outcomes in critical situations. By bridging the gap between advanced technology and emergency services, this research contributes to the advancement of road safety initiatives and underscores the importance of leveraging computational tools for addressing pressing societal challenges.
of accident detection and emergency response technologies, the proposed system utilizes Python programming language and sensor data to autonomously identify accident events. Upon detection, the system promptly sends emergency signals with precise location information to relevant authorities, facilitating swift response and potentially saving lives. Through rigorous testing and validation, the effectiveness and reliability of the proposed system are evaluated, highlighting its potential to significantly improve emergency response capabilities. By bridging the gap between advanced technology and emergency services, this research aims to contribute to the advancement of road safety initiatives and underscores the importance of leveraging computational tools for addressing critical societal challenges.
KEYWORDS: Python, Django, Sql, TensorFlow, Open CV, Html, CSS.
2. LITERATURE REVIEW: The literature on accident detection systems showcases a variety of approaches aimed at improving road safety. Existing systems typically rely on technologies such as computer vision, machine learning, and sensor networks to detect accidents in real-time. These systems offer features like automatic emergency alerts, location tracking, and communication with emergency services. However, challenges such as accuracy, scalability, and integration with existing infrastructure remain.
1. INTRODUCTION: Road accidents continue to pose a significant threat to public safety worldwide, claiming millions of lives and causing immense economic and social costs each year. Prompt and effective emergency response is crucial in mitigating the impact of accidents and saving lives. However, traditional emergency response systems often rely on manual reporting, leading to delays in response times and hindering the timely delivery of critical assistance.
The existing systems are: The literature review explores the rising concern of vehicular accidents due to increasing road traffic, especially during bad weather conditions. It discusses existing methods for accident detection, emphasizing the limitations of relying solely on vehicle sensors or smartphone-based systems. The research proposes a novel framework of smart roads equipped with multiple sensors to autonomously detect accidents, alert approaching vehicles, and notify Emergency Operations Centers (EOCs) without relying on vehicular communication.[1]
To address this challenge, there is a growing interest in leveraging advanced technologies, such as sensor data and machine learning algorithms, to develop automated accident detection systems. These systems have the potential to detect accidents in real-time and automatically notify emergency services, enabling faster response and potentially reducing the severity of injuries and fatalities.In this context, this paper presents the development and implementation of an accident detection software system designed to enhance emergency response to road accidents. Building upon prior research in the field
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