Title: IoT-based vehicular accident detection using a deep learning model

Authors: Ishu Rani; Bhushan Thakre; K. Jairam Naik

Addresses: Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur – 492010, Chhattisgarh, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur – 492010, Chhattisgarh, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur – 492010, Chhattisgarh, India

Abstract: With the increase in population and running valuable time, the demand for cars has skyrocketed creating an unprecedented condition in spite of traffic risks and road collisions. The crashes are growing at an unprecedented pace; hence, they cause death. Since Machine Learning has taken over, previously complex problems have become feasible due to the promising real-life applications of these models. A learning model that learns over an image dataset, thereby classifying never-before-seen images and data based on the level of damage, has been proposed in this paper. The artificial neural network is used to train the model and to learn the similarities among images and accident data. The proposed solution is efficient as it was tried to improve the efficiency and accuracy of finding the polarity of images for the same order of dataset as compared to the existing work.

Keywords: vehicles; accident detection; classification; accuracy; deep learning; IoT; Internet of Things; training model; image polarity.

DOI: 10.1504/IJAACS.2024.135931

International Journal of Autonomous and Adaptive Communications Systems, 2024 Vol.17 No.1, pp.1 - 23

Received: 13 Aug 2021
Accepted: 11 Nov 2021
Published online: 10 Jan 2024 *

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