The rapid and accurate classification of accident severity plays a vital role in enhancing emergency response mechanisms, optimizing resource allocation, and improving road safety outcomes. In this paper, we present a deep learning-based accident classification system designed to automatically evaluate and categorize accident severity using image data. Our system leverage a custom convolutional neural network (CNN) architecture composed of multiple convolutional and pooling layers, integrated with batch normalization to enhance feature extraction and model stability. The model was trained and validated on a structured dataset of road accident images divided into multiple severity classes, utilizing a well-optimized training pipeline that includes data augmentation, caching, and prefetching strategies to improve performance. We conducted rigorous experimentation using a dataset split into training, validation, and testing subsets, and achieved an overall classification accuracy exceeding 90\%. The proposed model architecture demonstrated robust performance across varying accident types and lighting conditions, showcasing its generalizability and reliability. Comparative analysis against conventional methods highlights the benefits of automation in reducing human bias and accelerating incident assessment. Moreover, the study explores practical applications of this system in real-world scenarios such as real-time traffic surveillance, automated emergency dispatch systems, and insurance claim evaluation platforms. The paper also outlines the deployment considerations, including model serialization, checkpointing for best performance, and visualization of training statistics for continuous improvement. Despite its promising results, the system faces challenges in extreme weather scenarios or highly occluded scenes, which we address as part of the future work roadmap. This research contributes significantly to the domain of intelligent transportation systems and public safety by illustrating the potential of computer vision and deep learning in accident severity assessment.
Pathak et al. (Sat,) studied this question.