Emergency Vehicle Detection Using Deep Convolutional Neural Network

Haque, Samiul; Sharmin, Shayla; Deb, Kaushik

doi:10.1007/978-981-19-0332-8_40

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…According to the different number of tracked targets, it can be divided into single-object tracking (SOT) and multi-object tracking (MOT). Due to the mutual influence of multiple tracks, multi target tracking is much more complex than single target tracking [7][8][9][10]. The detection of traffic targets in the scene of boarding and disembarking ferries is a typical multi-objective tracking problem.…”

Section: Comparison and Selection Of Traffic Target Tracking Methodsmentioning

confidence: 99%

A Risk Management Method for the Whole Route of Ferry Vehicles Based on Machine Recognition

2023

AJCIS

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In response to the current information acquisition methods in the safety management process of crossing transportation vehicles, backwardness of backwardness, large manpower and material consumption, and video surveillance resources are not fully utilized, this article promotes the in -depth integration of artificial intelligence machine vision technology with the ferry business through the automatic analysis and processing technology of monitoring videos. It realizes the real-time monitoring of the operating status of the ferry vehicle and the refined control of dangerous events, providing new means for the operation monitoring of crossing vehicles, and effectively supporting the high -quality development of ferry transportation.

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Section: Comparison and Selection Of Traffic Target Tracking Methodsmentioning

confidence: 99%

A Risk Management Method for the Whole Route of Ferry Vehicles Based on Machine Recognition

2023

AJCIS

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“…According to the experiment, the proposed approach achieved an accuracy of 95%. While in, Haque et al [4] developed and implemented an automated model for emergency vehicle detection based on emergency (ambulance and fire trucks) and non-emergency (other vehicles). YOLOv4 is first used for object detection using the region of interest (RoI) strategy and then the detected objects are trained using CNN and VGG-16 by fine tuning of model parameters.…”

Section: Related Workmentioning

confidence: 99%

“…The CNN usually utilizes the approach of kernel filtering to process the image and the kernel's values are learned during the training phase. A deep CNN, which is constructed of stacking many layers, it has two limitations; it is difficult to train such network and it requires huge data to train it [4].…”

Section: Introductionmentioning

confidence: 99%

Emergency vehicles classification for traffic signal system using optimized transfer DenseNet201 model

Manguri,

Mohammed

2023

IJEECS

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<span>As a result of the rapid growth of the world population, traffic signaling systems for monitoring and controlling the roads have turned to be an important issue facing humanity. To effectively overcome this problem, an accurate method for congestion reduction on the roads should be used which has a direct relation between the population and the cars’ usage. Various approaches derived from deep and transfer learning have been investigated in this context. This research implemented an optimized transfer learning approach for densely connected convolutional neural network (DenseNet201) models for multiple classifications (non-emergency cars, ambulance, police, and firefighter). Due to the non-availability of public datasets, a customized dataset has been created. This paper aims to improve the performance accuracy of vehicle classification using certain preprocessing algorithms on the input images and testing various optimization methods. The performance accuracy of the proposed model is evaluated using k-folds cross-validation 20:80 for the test and training, respectively. The metrics which are used for comparison with other deep learning techniques are based on exactness, recall, accuracy and F1-score. Test outcomes specify that the proposed transfer model-based optimization outperforms alternative deep learning algorithms regarding vehicle accuracy in classifying and reaches 98.6%.</span>

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Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

Shetty,

Kodipalli

et al. 2023

2023 International Conference on Network, Multimedia and Information Technology (NMITCON)

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Emergency Vehicle Detection Using Deep Convolutional Neural Network

Cited by 3 publications

References 14 publications

A Risk Management Method for the Whole Route of Ferry Vehicles Based on Machine Recognition

A Risk Management Method for the Whole Route of Ferry Vehicles Based on Machine Recognition

Emergency vehicles classification for traffic signal system using optimized transfer DenseNet201 model

Emergency vs Non-Emergency Vehicle Classification: Enhancing Intelligent Traffic Management Systems

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