REAL TIME ACCIDENT DETECTION AND REPORTING SYSTEM USING CNN ALGORITHM
DOI:
https://doi.org/10.63458/ijerst.v2i2.82Keywords:
Machine learning, Accident detection, Feature extraction, Image classification, Twilio, SMSalert, CNN algorithm.Abstract
In order to identify patterns for decision-making, deep learning, a branch of artificial intelligence, imitates how the human brain processes data. Within machine learning, it utilizes networks that discern and categorize patterns from unstructured or untagged data. Referred to as deep neural learning, Convolutional Neural Networks (ConvNets or CNNs) shine in image recognition tasks, adept at identifying faces, objects, and traffic signs. Their prowess extends to robotics, enhancing vision for self-driving cars. Despite widespread awareness of driving regulations, a substantial number globally fall victim to vehicle crash injuries due to drivers' negligence, despite their knowledge. This paper contributes to road accident detection through the Mask R- CNN method, aiming to improve safety measures.
References
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Rohan Chorada, Hitesh Kriplani,Biswaranjan Acharya,’ CNN-based Real-time Pothole Detection for Avoidance Road Accident’, ICICCS,IEEE Xplore 2023.
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