ResQAlert: Proficient Crash Detection and Alert System

Abstract

Abstract: In response to the urgent need for automated vehicle crash detection and its potential to increase road safety while reducing crash severity, this study introduces an innovative and novel approach. It focuses on using deep learning techniques for image recognition in videos to predict the likelihood of an accident. Specifically, a convolutional neural network (CNN) model was carefully constructed using TensorFlow and Keras and trained on a diverse and carefully annotated dataset covering a wide range of road scenarios. These scenarios include different conditions such as the presence of vehicles, pedestrians and different road conditions. To ensure maximum model performance, the model is optimized with the Adam optimizer and includes training using sparse stratified cross-entropy loss. Furthermore, Checkpoint model calls are carefully used to protect the best models during the training process. The overall objective of this research project is to provide an efficient and accurate real-time solution for collision detection and the ultimate goal is to make a significant contribution. This will lead to improved traffic safety. This has the potential not only to prevent accidents, but also to reduce their severity, thereby significantly improving the safety and efficiency of transportation and ultimately improving the overall well-being of society.