Leveraging Deep Learning for Precision-Aware Road Accident Detection

Accident detection plays a critical role in improving traffic safety by enabling timely emergency response and reducing the impact of road incidents. The main challenge lies in achieving real-time, reliable and highly accurate detection across diverse Internet-of-vehicles (IoV) environments. To overcome this challenge, this paper leverages deep learning to automatically learn patterns from visual data to detect accidents with high accuracy. A visual classification model based on the ResNet-50 architecture is presented for distinguishing between accident and non-accident images. The model is trained and tested on a labeled dataset and achieves an overall accuracy of 91.84%, with a precision of 94%, recall of 90.38%, and an F1-score of 92.14%. Training behavior is observed over 100 epochs, where the model has shown rapid accuracy gains and loss reduction within the first 30 epochs, followed by gradual stabilization. Accuracy plateaues between 90−93%, and loss values remain consistent between 0.1 and 0.2 in later stages. To understand the effect of training strategy, the model is optimized using three different algorithms, namely, SGD, Adam, and Adadelta with all showing effective performance, though with varied convergence patterns. Further, to test its effectiveness, the proposed model is compared with existing models. In the end, the problems encountered in implementing the model in practical automotive settings and offered solutions are discussed. The results support the reliability of the approach and its suitability for real-time traffic safety applications.