• Aarti Swami
• Shrikant Shinde
• Snehal Birajdar
• Vishal Patil
• Vaishnavi Kshirsagar

Deep learning is important for accident detection because it can analyze large amounts of data, such as images or sensor readings from cameras or other devices, to identify patterns indicative of accidents or potential hazards. In this paper, we present a critical analysis of various existing methodologies used for predicting and preventing road accidents, highlighting their strengths, limitations, and challenges that need to be addressed to ensure road safety and save valuable lives. A Convolutional Neural Network is a type of artificial intelligence model that’s particularly good at processing and analysing visual data, such as images and video. In the context of automatic accident detection systems, it can be used to analyse images or video footage from cameras installed on roads and vehicles. CNNs are often considered better than FPNs and RNNs for certain tasks due to their ability to efficiently capture spatial hierarchies in data like images. FPNs are designed to address scale variation in object detection tasks but may not capture spatial information as effectively as CNNs. RNNs, on the other hand, are better suited for sequential data like text or time-series data but may struggle with capturing spatial relationships in data like images. So, for tasks where spatial hierarchies are crucial, CNNs often outperform FPNs and RNNs.

Convolutional Neural Network, Feature extraction, FPN, RNN Accident detection, Road safety, Accident detection.

"A Proactive Accident Detection And Prevention System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.c161-c165, March-2024, Available :http://www.jetir.org/papers/JETIR2403219.pdf

"A Proactive Accident Detection And Prevention System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppc161-c165, March-2024, Available at : http://www.jetir.org/papers/JETIR2403219.pdf