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Title: :  Emergency Vehicle Detection using Deep Learning
PaperId: :  19882
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 2 2023
DUI:    16.0415/IJARIIE-19882
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
HARI VIGNESH KAnand Institute Of Higher Technology
MUSHARRAF UAnand Institute Of Higher Technology
Balaji AAnand Institute Of Higher Technology

Abstract

Deep Learning
Vehicle detection, Xception model, Heavy Traffic analysis , Vehicle Datasets, Deep learning.
Emergency vehicle detection in heavy traffic can be challenging due to the presence of multiple vehicles and occlusions. However, using deep learning and computer vision techniques, we can develop an effective solution to this problem. The traffic congestion leads to high impact for emergency vehicles to pass by the particular road. The congestion can be deleted by many ways where in the video analytics-based detection and give more accuracy and able to count the type of vehicle in the particular traffic signals. The early detection of emergency vehicle in the particular signal can be avoid delay in transition. To overcome this problem, the proposed model that detects the emergency vehicle in the signal and pass the information to the signal regulating authorities to green corridor can be generated have on the information extracted from the video signals. One approach is to use a deep Convolutional Neural Network (CNN) to classify emergency vehicles in the video feed. CNNs are well-suited for image-based classification tasks and have shown promising results in object detection and recognition. To implement this approach, we can start by collecting a dataset of images or videos containing emergency vehicles in heavy traffic. The dataset should include a variety of scenarios with different lighting conditions, vehicle types, and traffic densities. The dataset is gathered and the model constructed with the cnn xception architecture. The RMSprop optimizer is used which is similar to the gradient descent algorithm with momentum. The RMSprop optimizer restricts the oscillations in the vertical direction. Therefore, we can increase our learning rate and our algorithm could take larger steps in the horizontal direction converging faster. For object recognition with a CNN, we freeze the early convolutional layers of the network and only train the last few layers which make a prediction with transfer learning. Finally, using Flask framework and CNN Xception architecture with RMSprop optimizer and transfer learning, we can develop a model that can detect emergency vehicles in traffic signals and generate green corridors for them, thus reducing the delay in transition and improving the efficiency of emergency services.

Citations

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IJARIIE HARI VIGNESH K, MUSHARRAF U, and Balaji A. "Emergency Vehicle Detection using Deep Learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 2 2023 Page 2813-2819
MLA HARI VIGNESH K, MUSHARRAF U, and Balaji A. "Emergency Vehicle Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education 9.2(2023) : 2813-2819.
APA HARI VIGNESH K, MUSHARRAF U, & Balaji A. (2023). Emergency Vehicle Detection using Deep Learning. International Journal Of Advance Research And Innovative Ideas In Education, 9(2), 2813-2819.
Chicago HARI VIGNESH K, MUSHARRAF U, and Balaji A. "Emergency Vehicle Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 2 (2023) : 2813-2819.
Oxford HARI VIGNESH K, MUSHARRAF U, and Balaji A. 'Emergency Vehicle Detection using Deep Learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, 2023, p. 2813-2819. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Emergency_Vehicle_Detection_using_Deep_Learning_ijariie19882.pdf (Accessed : ).
Harvard HARI VIGNESH K, MUSHARRAF U, and Balaji A. (2023) 'Emergency Vehicle Detection using Deep Learning', International Journal Of Advance Research And Innovative Ideas In Education, 9(2), pp. 2813-2819IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Emergency_Vehicle_Detection_using_Deep_Learning_ijariie19882.pdf (Accessed : )
IEEE HARI VIGNESH K, MUSHARRAF U, and Balaji A, "Emergency Vehicle Detection using Deep Learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, pp. 2813-2819, Mar-App 2023. [Online]. Available: http://ijariie.com/AdminUploadPdf/Emergency_Vehicle_Detection_using_Deep_Learning_ijariie19882.pdf [Accessed : ].
Turabian HARI VIGNESH K, MUSHARRAF U, and Balaji A. "Emergency Vehicle Detection using Deep Learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 2 ().
Vancouver HARI VIGNESH K, MUSHARRAF U, and Balaji A. Emergency Vehicle Detection using Deep Learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(2) : 2813-2819. Available from: http://ijariie.com/AdminUploadPdf/Emergency_Vehicle_Detection_using_Deep_Learning_ijariie19882.pdf
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