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Accident Detection Using Mask R-CNN

Akshit Diwan Vandit Gupta and Chaitanya Chadha

2021 · DOI: 10.46501/ijmtst070115
6 Citations

TLDR

This paper helps in the detection of road accidents using the Mask R-CNN approach that imitates the workings of the human brain in processing data and creating patterns for use in decision making.

Abstract

Deep learning is an artificial intelligence function that imitates the workings of the human brain in

processing data and creating patterns for use in decision making. Deep learning is a subset of machine

learning in artificial intelligence (AI) that has networks capable of learning and recognizing patterns from

data that is unstructured or unlabeled. It is also known as deep neural learning or deep neural network.

Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very

effective in areas such as image recognition and classification. ConvNets have been successful in identifying

faces, objects and traffic signs apart from powering vision in robots and self-driving cars.

Consistently around the globe, an enormous number of individuals pass on from vehicle crash wounds. A

large portion of the drivers are very much aware of the overall principles and security measures while driving

yet it is just the laxity on their part, which causes mishaps and accidents. This paper helps in the detection of

road accidents using the Mask R-CNN approach.