AI for Safer Streets: Deep Learning-Based Road Accident Detection from Video Footage

The detection of road accidents is of great concern across the world. To put a stop to all the tragedy that ensues, we need efficient and workable monitoring and detection systems. In this paper, results for recognizing road accidents through the analysis of a holistic image database from camera surveillance with balanced classes are presented. 19 layers of Convolutional Neural Network (CNN) designed in the light is also built and trained on these images for accurate accident detection. When trained, the model exhibits robust learning capacity and its training or validation accuracy nigh reached $\mathbf{9 4 \%}$ and $\mathbf{9 7. 6 7 \%}$, a great achievement. This suggests that it can generalize well to capture key features indicative of road accidents in CCTV footage. After the training stage is complete, the model is tested on a separate test dataset where it struts its stuff and achieves an impressive test accuracy of $\mathbf{9 8 \%}$. This high degree of accuracy means that it can accurately recognize road accidents from CCTV images, which is critical for prompt response and amelioration measures. In addition, the model’s Receiver Operating Characteristic (ROC) analysis presents some data: recovered in ROC values--0.92 for accident class and 0.87 for other classes (non-accident instances). This further testifies to the model’s capacity in real-world accident detection situations as well as its unambiguity among different types of accidents. Overall, our study illustrates the effectiveness of deep-learning technology applied to the road accident detection such as a 19-layer CNN model. Consistent high accuracy rates and robust ROC values are indicators that show great promise for how widely it might ultimately be used to enhance safety on our roads through automated accident detection systems.