Emergency Vehicle Detection Research Articles

Differential Analysis of Emergency Vehicle Detection in Urban Traffic : A Systematic Review

The differential analysis of emergency vehicle detection in urban traffic is crucial for improving response times and reducing traffic-related delays during emergencies. This systematic review aims to analyze various methods and technologies, such as acoustic, visual, and sensor-based systems, used for detecting emergency vehicles in complex urban environments. The motivation behind this study is the growing need for more efficient traffic management systems that prioritize emergency vehicles, minimizing delays caused by congestion. However, limitations include the inconsistent performance of detection systems in varying weather, lighting, and noise conditions, as well as integration challenges with existing infrastructure. The objective is to evaluate current detection methods, identify their limitations, and propose potential improvements to enhance the accuracy and reliability of emergency vehicle detection in urban traffic systems.

An Enhanced Model for Detecting and Classifying Emergency Vehicles Using a Generative Adversarial Network (GAN)

The rise in autonomous vehicles further impacts road networks and driving conditions over the road networks. Cameras and sensors allow these vehicles to gather the characteristics of their surrounding traffic. One crucial factor in this environment is the appearance of emergency vehicles, which require special rules and priorities. Machine learning and deep learning techniques are used to develop intelligent models for detecting emergency vehicles from images. Vehicles use this model to analyze regularly captured road environment photos, requiring swift actions for safety on road networks. In this work, we mainly developed a Generative Adversarial Network (GAN) model that generates new emergency vehicles. This is to introduce a comprehensive expanded dataset that assists emergency vehicles detection and classification processes. Then, using Convolutional Neural Networks (CNNs), we constructed a vehicle detection model demonstrating satisfactory performance in identifying emergency vehicles. The detection model yielded an accuracy of 90.9% using the newly generated dataset. To ensure the reliability of the dataset, we employed 10-fold cross-validation, achieving accuracy exceeding 87%. Our work highlights the significance of accurate datasets in developing intelligent models for emergency vehicle detection. Finally, we validated the accuracy of our model using an external dataset. We compared our proposed model’s performance against four other online models, all evaluated using the same external dataset. Our proposed model achieved an accuracy of 85% on the external dataset.

Density Based Real-time Smart Traffic Management System along with Emergency Vehicle Detection for Smart Cities

Density Based Real-time Smart Traffic Management System along with Emergency Vehicle Detection for Smart Cities

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems.

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

TRAFFIC PULSE-ROADWAY CONTROL SYSTEM

Rapid transit and transportation networks are essential to any country's economic progress. Long wait times, lost fuel, and financial losses are the outcomes of poor management and traffic congestion. For the sake of national development, a quick, affordable, and effective traffic control system is thus absolutely essential. In many nations, keeping an eye on and managing urban traffic is turning into a significant issue. The Traffic Monitoring Authority needs to come up with innovative ways to solve this issue because there are more and more cars on the road. Using automation and intelligent control techniques is one option to enhance the current transportation system's traffic flow and safety. Efficient and quick congestion relief is the aim. The project includes sound sensors that can recognize emergency cars as they approach in order to improve emergency vehicle detection. By prioritizing and streamlining emergency vehicle traffic at signalized junctions, this cutting-edge feature seeks to enhance overall traffic management and response times. Additionally, the concept has a novel element for pedestrian safety: when a signal changes from red to orange or orange to green, a dynamically triggered block is deployed. By erecting a barrier that guarantees pedestrians are securely clear of traffic before crossing, this creative solution seeks to improve pedestrian safety. Keywords: Traffic jams, traffic surveillance, automation, and clever control techniques, Changes in signals, sound sensors, emergency vehicle detection, quicker reaction times, barriers, and pedestrian safety.

Acoustic Spectral Analysis for Emergency Vehicle Detection: Symbolic and CNN Approaches

During emergencies, ambulances on city streets face delays due to traffic obstacles. This paper addresses two efficient emergency vehicle detection (EVD) methods for restricted hardware implementation considering noisy conditions: a symbolic processing-based algorithm and a convolutional neural network (CNN) model, both of which utilize Mel spectrogram representations of Hi-Lo siren audio records. The symbolic method employs regular expressions and acceptance criteria to process text-pattern features extracted from spectrograms, offering a self-explanatory, easily tunable, and resource-efficient solution suitable for lowcost hardware platforms. On the other hand, the CNN model directly processes spectrogram representations, leveraging spatial correlation for classification with a streamlined architecture consisting of very few layers. The experimental results demonstrate that both approaches achieve high accuracy (97-98%) in classifying Hi-Lo sirens, with the CNN model exhibiting slightly better performance. Challenges such as signal noise and harmonics are addressed through iterative algorithms and signal reconstruction considerations. Future directions include identifying additional siren effects and conducting performance measurements on constrained hardware devices. Overall, this study presents viable EVD solutions suitable for real-time implementation and underscores the importance of adaptable and explainable AI methods in enhancing road safety.

Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System

The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing technologies as its foundation. This research addresses to innovate an advanced smart crosswalk consisting of four essential components: a real-time Pedestrian Detection and Priority System customized for individuals with special needs, a responsive system for detecting road conditions, vehicle availability and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by rigorous verification procedures, and a robust framework for identifying pedestrian accidents and violations of crosswalk rules. The entire system has been meticulously designed not only to enhance pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to provide an improved pedestrian crossing experience characterized by increased safety and efficiency.

Detecting emergency vehicles With 1D-CNN using fourier processed audio signals

Emergency Vehicle Detection (EVD) is a critical task in transportation management. Vehicle audio analysis provides affordable and insightful data, compared to established yet expensive vehicle detection methods. This research presents two contributions. Firstly, to decrease the processing power and memory demands, it offers a method to detect the optimal maximum sampling frequencies through an analysis of correlation between spectral entropy (SE) and dataset variance, grounded in the principles of the discrete Fourier transform (DFT) and Nyquist–Shannon theorem. Secondly, to address the inconsistency between recorded data in different datasets, this paper proposes segmenting audio into small input time windows (ITW) and applying individualized normalization. Training the model on dataset-1 achieves 98.37% accuracy on the validation set, with 86.83% accuracy on dataset-2. Comparative analysis with the baseline Long-Short-Term Memory model shows 22.79% performance improvement in favor of the proposed 1D-CNN model. Overall, this model outperforms state-of-the-art techniques, achieving accuracy rate of 98.37%.

A Comparative Study of Neural Networks for Emergency Vehicle Detection in Traffic

This study explores the application of Neural Networks for efficient emergency vehicle identification. Utilizing a dataset of emergency and non-emergency vehicle images, the model demonstrates high accuracy in classification. A noteworthy use case involves real-time integration with traffic systems, where the identification of an emergency vehicle triggers an automatic change of traffic lights to green, facilitating swift and unimpeded passage.

Review of Emergency Vehicle Detection Techniques by Acoustic Signals

Review of Emergency Vehicle Detection Techniques by Acoustic Signals

A Review of Computer Vision–Based Traffic Controlling and Monitoring

Due to the rapid increase of the population in the world, traffic signal controlling and monitoring has become an important issue to be solved with regard to the direct relation between the number of populations and the cars’ usage. In this regard, an intelligent traffic signaling with a rapid urbanization is required to prevent the traffic congestions, cost reduction, minimization in travel time, and CO2 emissions to atmosphere. This paper provides a comprehensive review of computer vision techniques for autonomic traffic control and monitoring. Moreover, recent published articles in four related topics including density estimation investigation, traffic sign detection and recognition, accident detection, and emergency vehicle detection are investigated. The conducted survey shows that there is no fair comparison and performance evaluation due to the large number of involved parameters in the abovementioned four topics which can control the traffic signal controlling system such as (computation time, dataset availability, and an accuracy).

Using convolutional neural networks for acoustic-based emergency vehicle detection

Background: A siren is a special signal given by emergency vehicles such as fire trucks, police cars and ambulances to warn drivers or pedestrians on the road. However, drivers sometimes may not hear the siren due to the sound insulation of a modern car, the noise of city traffic, or their own inattention. This problem can lead to a delay in the provision of emergency services or even to traffic accidents.
 Aim: develop an acoustic method for detecting the presence of emergency vehicles on the road through the use of convolutional neural networks.
 Materials and Methods: The algorithm of work is based on the conversion of sound from the external environment into its spectrogram, for analysis by a convolutional neural network. An open dataset (Emergency Vehicle Siren Sounds) from sources available on Internet sites such as Google and Youtube, saved in the .wav audio format, was used as a dataset for siren sounds and city traffic. The code was developed on the Google.Colab platform using cloud storage.
 Results: The conducted experiments showed that the proposed method and model of the neural network make it possible to achieve an average efficiency of determining the type of sound with an accuracy of 93.3 % and a speed recognition of 0.00045 % of a second.
 Conclusion: The use of the developed technology for recognizing siren sounds in city noize will improve traffic safety and increase the chances of preventing a dangerous situation. Also, this system can be an additional assistant for hearing-impaired people while driving and everyday life for timely notification of the presence of emergency services nearby.

Automatic Traffic Management and Emergency Vehicle Detection

Automatic Traffic Management and Emergency Vehicle Detection

Emergency Vehicle Detection in Heavy Traffic using Deep ConvNet2D and ComputerVision

Emergency Vehicle Detection in Heavy Traffic using Deep ConvNet2D and ComputerVision

Acoustic Based Emergency Vehicle Detection Using Ensemble of deep Learning Models

The temporal and spectral structure is possessed in the time-frequency domain by sound events. Analyzing and classifying acoustic environment using sound recording is an emerging research area. Convolutional layers can quickly extract high-level features and shift-invariant features from the time-frequency domain. In this work, emergency vehicle detection (EVD) like fire brigades, ambulances, and police cars is done based upon their siren sounds. Dataset from Google Audioset ontology was collected and features are extracted by Mel-frequency Cepstral Coefficient (MFCC). Three deep neural networks (DNN) models (dense layer, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)) with different configurations and parameters have been investigated. Then, an ensemble model has been designed with optimum selected models by performing experimental tests on various configurations with hyper-parameter tuning. The proposed ensemble model provides the highest accuracy of 98.7%, while the recurrent neural network (RNN) model provides an accuracy of 94.5%. Also, performance analysis of deep learning models is done with various machine learning models like Perceptron, SVM, decision tree etc.

Few-Shot Emergency Siren Detection.

It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This paper proposes a workflow based on few-shot metric learning for emergency siren detection performed in steps: prototypical networks are trained on publicly available sources or synthetic data in multiple combinations, and at inference time, the best knowledge learned in associating a sound with its class representation is transferred to identify ambulance sirens, given only a few instances for the prototype computation. Performance is evaluated on siren recordings acquired by sensors inside and outside the cabin of an equipped car, investigating the contribution of filtering techniques for background noise reduction. The results show the effectiveness of the proposed approach, achieving AUPRC scores equal to 0.86 and 0.91 in unfiltered and filtered conditions, respectively, outperforming a convolutional baseline model with and without fine-tuning for domain adaptation. Extensive experiments conducted on several recording sensor placements prove that few-shot learning is a reliable technique even in real-world scenarios and gives valuable insights for developing an in-car emergency vehicle detection system.

A Transfer-Learning-Based Approach for Emergency Vehicle Detection

A Transfer-Learning-Based Approach for Emergency Vehicle Detection

Density Based Emergency Vehicle Detection and Traffic Signal Controlling using Image Processing

In present day generation, the number of vehicles is increased because of increase in population. Hence in this paper, the design of vehicle detection and traffic control signal is implemented by using processing of image. The proposed system main intent is to acquire the images for the vehicle and control the traffic signal. Firstly an image is captures from the web camera, which is placed in traffic control area on the road. After this the traffic density is calculated from the obtained images. Basically, this image enhancement performs its operation in two forms they are operating phase and learning phase. Here the captured image as enhanced by using the image enhancement method. Hence the main advantage of this proposes system is that it processes the entire operation in simple way with high speed. The proposed system will capture the images of road areas in effective way. Hence the propose system has various features which will determine the color, width and many other. The proposed implemented system is mat lab software to prevent congestion of heavy traffic.

Density based Traffic Control System Embedded with Patient Weightage Method

Every now and then traffic congestion has always been a hindrance in everyone’s normal life routine. This traffic congestion hindrance is very much problematic in case of high priority emergency vehicles namely ambulances, VIP vehicles, fire engines etc. So, traffic control has to be made proficient to provide smooth flow of vehicles. However, efficient synchronization of traffic at multiple junctions is complex. Conventional control systems do not handle the dynamic flow of traffic which results in chaos. In this project, implementation of an emergency vehicle detection system is done. The main objective is to provide a traffic-free route to the vehicles in order to save life. In this paper two cases are considered, in the initial case, the lane density at the junction will be calculated using ultrasonic sensors by Raspberry pi (Rpi) and the lane with minimal density route will be directed to the emergency vehicle NodeMcu in this case. Considering the assumption that hospital is present near to every lane. And in the second case, the conflict arises when two emergency vehicles are encountered at the same time at a junction, so this conflict is resolved in thispaper.