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AI and IOT Based Road Accident Detection and Reporting System

Profile image of IJRASET PublicationIJRASET Publication

2023, International Journal for Research in Applied Science & Engineering Technology (IJRASET)

https://doi.org/10.22214/IJRASET.2023.48904
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13 pages

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Abstract

Road accidents are increasing daily as the number of automobiles rises. An annual global death toll of 1.4 million and an injury toll of 50 million are reported by the World Health Organization (WHO). The absence of medical assistance at the scene of the accident or the lengthy response time during the rescue effort are the main causes of mortality. We can reduce delays in a rescue operation that has the potential to save many lives by using a cognitive agent-based collision detection and smart accident alarm and rescue system. To gather and send accident-related data to the cloud or server, the suggested system consists of a force sensor, GPS module, alarm controller, ESP8266 controller, camera, Raspberry Pi, and GSM module. The accident is then verified using cloud-based techniques for deep learning. accident is then verified using cloud-based techniques for deep learning. When the deep learning module notices an accident, it immediately alerts all nearby emergency services, including the hospital, police station, mechanics, etc.

Figures (8)
Fig.2 Proposed Architecture of Accident Detection and Reporting System [1]. IV. DETAILS OF PROPOSED ACCIDENT DETECTION AND REPORTING SYSTEM Working of proposed system is take place in two main parts: Fig.1 Working of Accident Detection and Reporting System
Fig.2 Proposed Architecture of Accident Detection and Reporting System [1]. IV. DETAILS OF PROPOSED ACCIDENT DETECTION AND REPORTING SYSTEM Working of proposed system is take place in two main parts: Fig.1 Working of Accident Detection and Reporting System
Fig.3 Deep learning architecture of proposed ADRS [1] = = Over the past few decades, deep learning algorithms have undergone significant development and may currently be utilised with excellent accuracy [9-13] in any field. The majority of the ADRS now in use only identify the accidents using deep learning or IoT sensors. As a result, information is sent to all emergency numbers as soon as an accident is discovered. Since these methods rely solely on the sensor, they might have a greater false detection rate. Therefore, the existing systems recognise a quick halt by the operator of their vehicle as an accident. We are utilsing a transfer learning-based pre-trained model called Res Net anc InceptionResnetV2 to reduce the false detection rate. These models have been taught to divide the input video into two categories, namely accidents and non-accidents. If the model’s output is accident, then only the accident location is shared with the emergency services[ 1].
Fig.3 Deep learning architecture of proposed ADRS [1] = = Over the past few decades, deep learning algorithms have undergone significant development and may currently be utilised with excellent accuracy [9-13] in any field. The majority of the ADRS now in use only identify the accidents using deep learning or IoT sensors. As a result, information is sent to all emergency numbers as soon as an accident is discovered. Since these methods rely solely on the sensor, they might have a greater false detection rate. Therefore, the existing systems recognise a quick halt by the operator of their vehicle as an accident. We are utilsing a transfer learning-based pre-trained model called Res Net anc InceptionResnetV2 to reduce the false detection rate. These models have been taught to divide the input video into two categories, namely accidents and non-accidents. If the model’s output is accident, then only the accident location is shared with the emergency services[ 1].
Fig.4 ResNet-50 layered architecture (He K. et al., 2016 [1]). NE NESE NEE IED NN IE The training and accuracy of the deep learning-based model depend on the quantity and quality of the data because deep learning models need a lot of data. An IoT kit that gathers real-time data from the accident site and sends it to the cloud for further processing can be created in order to receive high-quality, real-time data. When we only have a little dataset, one of the most effective options is the pre-trained model.[1] These models are more accurate because to their well-trained accuracy on a large dataset. As a result, we only need to train these models for our particular objective. As a result, a pre-trained model can be trained with greater accuracy on a smaller dataset. There are many models that have already been trained, including VGGNet, ResNet-50, InceptionNet, InceptionResNetV2, etc.
Fig.4 ResNet-50 layered architecture (He K. et al., 2016 [1]). NE NESE NEE IED NN IE The training and accuracy of the deep learning-based model depend on the quantity and quality of the data because deep learning models need a lot of data. An IoT kit that gathers real-time data from the accident site and sends it to the cloud for further processing can be created in order to receive high-quality, real-time data. When we only have a little dataset, one of the most effective options is the pre-trained model.[1] These models are more accurate because to their well-trained accuracy on a large dataset. As a result, we only need to train these models for our particular objective. As a result, a pre-trained model can be trained with greater accuracy on a smaller dataset. There are many models that have already been trained, including VGGNet, ResNet-50, InceptionNet, InceptionResNetV2, etc.
Fig.5 InceptionResNetV2 layered architecture (Szegedy C. et al., 2017 [1]).
Fig.5 InceptionResNetV2 layered architecture (Szegedy C. et al., 2017 [1]).
Fig.6 Flowchart of the proposed Accident Detection and Reporting System (ADRS)
Fig.6 Flowchart of the proposed Accident Detection and Reporting System (ADRS)
Fig.8 Accident Prediction Result(b)(Visual Studio Code IDE)
Fig.8 Accident Prediction Result(b)(Visual Studio Code IDE)
Fig.9 No-Accident Prediction Result(b)(Visual Studio Code IDE)
Fig.9 No-Accident Prediction Result(b)(Visual Studio Code IDE)

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