Car Crash Detection System using Machine Learning and Deep Learning Algorithms

Abstract

Over 80% of mishaps are caused by a lack of identifying the accident on time, as well as failure to arrive in time to provide emergency care for the victim. The point is to distinguish and utilize machine learning to decide the best means of detecting car crash with light of the live transfer of dash cam data in the vehicle. The thought is to take every pixel and run it with a deep learning model prepared to recognize video outlines into mishap or non-mishap. Essentially, in Artificial Intelligence (AI), informational indexes are collected. Gathered information bases will be refined and given to AI calculations to prepare for image recognition with the help of computer vision. After fruitful preparation, AI calculations will be tried and the outcomes will be recorded for examination purposes. In the proposed vehicle crash location framework, the impact identification is performed utilizing the Convolutional Neural Network (CNN), taking a bunch of pictures as information, the framework distinguishes the crash, the effect of the vehicle and the greatness of the mishap. Based on the performance of machine learning algorithms, comparative analysis is performed and the results will be tabulated. Two machine learning algorithms are considered i.e. Random Forest Classifier and Logistic Regression which enables the output regarding the generated index from the CNN and running through the indices with location impact and severity of the damage.

Introduction

The text presents an AI-based automobile crash detection and severity assessment system designed to identify road accidents quickly, estimate their severity and point of impact, and support faster emergency response.

Main objective

Road accidents cause significant loss of life and property, and rapid reporting can help emergency services respond sooner. The proposed system uses computer vision, deep learning, and machine learning to detect crashes from images or video and determine characteristics such as:

• Whether an accident has occurred

• The vehicle's damage

• The first point of impact

• Crash severity

• Potential repair-cost information

• Accident location for notification purposes

Algorithms used

The system compares three main approaches:

• Convolutional Neural Network (CNN): processes images/video frames and identifies visual evidence of crashes and vehicle damage.

• Random Forest (RF): uses an ensemble of decision trees to predict crash-related characteristics from processed data.

• Logistic Regression (LR): provides a simpler statistical classification method for comparison.

The text also discusses VGG-16 transfer learning as a possible foundation for damaged-vehicle classification.

Proposed workflow

The system follows approximately this pipeline:

Camera/CCTV/Dashcam → Video frames → Preprocessing → Vehicle/Accident Detection → Damage & Impact Classification → Severity Estimation → ML Prediction → Emergency Notification

Images are first checked for vehicles and then categorized according to impact location and damage severity. The resulting information can be stored as annotations and used by machine-learning models to predict crash-related outcomes.

Methodology

The project involves:

1. Data acquisition: obtaining images or frames from crash-related videos.

2. Preprocessing: removing irrelevant information, handling missing/duplicate data, and organizing images.

3. CNN-based detection: extracting visual features and identifying accident-related patterns.

4. Damage and severity classification: determining the location and seriousness of vehicle damage.

5. Random Forest and Logistic Regression: predicting crash characteristics from structured data.

6. Emergency notification: sending accident information such as the time and location to relevant responders, potentially including hospitals and police.

Results and discussion

The text presents the system primarily as a comparative framework rather than reporting a complete set of experimental results for the proposed implementation. It states that CNN is intended for visual accident confirmation and assessment, while Random Forest and Logistic Regression are used for structured prediction.

The literature cited in the text reports results such as 90.02% average precision at 21.6 FPS for one accident-detection study and 95% accuracy for another accident-prediction task. These figures belong to the referenced studies and should not be interpreted as performance results of the proposed system.

Conclusion

This work presents a computer-vision-based framework for car-crash detection using machine learning and deep learning. The approach uses CNNs to analyze visual data and distinguish accident-related frames, while Random Forest and Logistic Regression provide additional prediction of crash characteristics such as impact location and severity. The source project further connects detection with real-time notification to emergency stakeholders. The overall workflow demonstrates how visual recognition can be integrated with structured machine learning and an alert mechanism for faster incident reporting. The supplied project report indicates that experimentation was carried out using images and that the proposed approach is intended for deployment on surveillance cameras. However, the conference version should report only experimentally measured values that are available from the actual implementation. Additional controlled evaluation is therefore needed to establish robust performance across datasets and road conditions. Future development can extend the system toward integrated CCTV and roadside monitoring, GPS-based location services, GSM or other communication modules, number-plate identification, driver-behavior or alcohol-related risk detection, and centralized emergency coordination. The source report also proposes extending the system beyond detection toward victim assistance and preventive alerts. Further work can investigate larger and more diverse video datasets, temporal video models, edge-device deployment, improved severity estimation, false-alarm reduction, and systematic evaluation using precision, recall, F1-score, sensitivity, specificity, latency, and frames-per-second measurements.

References

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Copyright

Copyright © 2026 Dr. S. Uma, Priyadharshini R. 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.