Abstract
In densely populated cities, emergency vehicles getting caught in traffic is a regular occurrence. As a result, emergency vehicles arrive late, resulting in asset and human life losses. It is critical to treat emergency vehicles differently to avoid losses. The purpose underlying this research is to preserve human lives and reduce losses. For this, an automated method for detecting emergency vehicles is implemented. Ambulance and fire trucks are considered an emergency, and other vehicles are considered non-emergency vehicles in the proposed method. Initially, it identifies several vehicles from an image. The YOLOv4 object detector accomplished this part of the method. The identified vehicles are the region of interest for the rest of the research. Finally, the method classifies the vehicles into emergencies or non-emergencies. This study contributes by developing a model based on rigorous testing and analysis and includes a viral algorithm in deep learning: convolutional neural network (CNN). Furthermore, the transfer learning technique with VGG16’s fine-tuned model is employed for emergency vehicle detection. On the Emergency Vehicle Identification v1 dataset, this model had an average accuracy of 82.03%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Emergency vehicles identification. https://www.kaggle.com/abhisheksinghblr/emergency-vehicles-identification. Accessed 2 June 2021
Bochkovskiy A, Wang C-Y, Liao HM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv:2004.10934
Chen L, Ye F, Ruan Y, Fan H, Chen Q (2018) An algorithm for highway vehicle detection based on convolutional neural network. EURASIP J Image Video Process 2018(1):1–7
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258 (2017)
Fan Q, Brown L, Smith J (2016) A closer look at faster R-CNN for vehicle detection. In: 2016 IEEE intelligent vehicles symposium (IV), pp 124–129. IEEE
Kanzaria HK, Probst MA, Hsia RY (2016) Emergency department death rates dropped by nearly, 50 percent 1997–2011. Health Affairs 35(7):1303–1308
Karter MJ (2011) Fire loss in the united states during 2010: national fire protection association quincy
Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516
Kiefer J, Wolfowitz J (1952) Stochastic estimation of the maximum of a regression function. Annals Math Stat 23(2):462–466
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980
Lin T-Y et al (2014) Microsoft COCO: common objects in context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) ECCV 2014, vol 8693. LNCS. Springer, Cham, pp 740–755. https://doi.org/10.1007/978-3-319-10602-1_48
Liu W et al (2016) SSD: single shot multibox detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) ECCV 2016, vol 9905. LNCS. Springer, Cham, pp 21–37. https://doi.org/10.1007/978-3-319-46448-0_2
Raman A, Kaushik S, Rajeswara Rao KVS, Moharir M (2020) A hybrid framework for expediting emergency vehicle movement on Indian roads. In: 2020 2nd international conference on innovative mechanisms for industry applications (ICIMIA), pp 459–464. IEEE
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788
Redmon J, Farhadi A (2018) YOLOV3: an incremental improvement. arXiv preprint arXiv:1804.02767
Robbins H, Monro S (1951) A stochastic approximation method. Annals Math Stat 22(3):400–407
Roy S, Rahman MdS (2019) Emergency vehicle detection on heavy traffic road from CCTV footage using deep convolutional neural network. In: 2019 international conference on electrical, computer and communication engineering (ECCE), pp 1–6. IEEE
Smeby LC (2021) Fire and emergency services administration: management and leadership practices includes navigate advantage access: management and leadership practices. Jones & Bartlett Learning, Burlington
Tarmizi IA, Aziz AA (2018) Vehicle detection using convolutional neural network for autonomous vehicles. In: 2018 international conference on intelligent and advanced system (ICIAS), pp 1–5. IEEE
Thomas A, Harikrishnan PM, Palanisamy P, Gopi VP (2020) Moving vehicle candidate recognition and classification using Inception-ResNet-v2. In: 2020 IEEE 44th annual computers, software, and applications conference (COMPSAC), pp 467–472. IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Haque, S., Sharmin, S., Deb, K. (2022). Emergency Vehicle Detection Using Deep Convolutional Neural Network. In: Uddin, M.S., Jamwal, P.K., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-0332-8_40
Download citation
DOI: https://doi.org/10.1007/978-981-19-0332-8_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0331-1
Online ISBN: 978-981-19-0332-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)