Development of a deep learning model for detection of road accidents
Abstract
This project aims to develop a deep learning model for the detection of road accidents.
The study focuses on leveraging the power of deep learning techniques to improve the
accuracy and efficiency of accident detection systems, ultimately contributing to
enhanced road safety. The methodology employed in this project involves the collection
of a diverse dataset consisting of images and videos captured from various traffic
scenarios. The dataset is annotated to label accident-related instances, enabling the
training of a deep learning model. Convolutional Neural Networks (CNNs) are utilized
to extract relevant features from the input data and identify patterns indicative of road
accidents. Transfer learning is applied to leverage pre-trained models and optimize the
training process. The developed model is deployed as a web application, allowing users
to access the accident detection system conveniently through web browsers.
Furthermore, a reporting system using SMS (Short Message Service) is incorporated to
enable automatic alerts and notifications to emergency services and relevant
stakeholders in real-time. The key results demonstrate the effectiveness of the
developed deep learning model in detecting road accidents. Through rigorous
evaluation on a large-scale dataset, the model achieves a high detection accuracy and
exhibits robust performance across different road and weather conditions. The model's
efficiency is also highlighted, as it provides real-time accident detection capabilities,
enabling prompt responses from emergency services. Based on the findings, the study
concludes that deep learning models can significantly contribute to the detection of road
accidents. By accurately identifying accidents, emergency services can be alerted
promptly, potentially reducing response times and improving overall road safety.
Furthermore, this project recommends the integration of the developed deep learning
model into existing traffic management systems. By deploying this model across
various road networks, authorities can enhance their accident detection capabilities and
provide better support to accident victim