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Smart city article details
| Title | Ai Enabled Accident Detection And Alert System Using Iot And Deep Learning For Smart Cities |
|---|---|
| ID_Doc | 6930 |
| Authors | Pathik N.; Gupta R.K.; Sahu Y.; Sharma A.; Masud M.; Baz M. |
| Year | 2022 |
| Published | Sustainability (Switzerland), 14, 13 |
| DOI | http://dx.doi.org/10.3390/su14137701 |
| Abstract | As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| Author Keywords | accident detection; AI; cognitive science; deep learning; InceptionResnetV2; intelligent transportation systems (ITS); IoT; ResNet; sensors |
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