Crash2DocAI: Automated Integration of Post-Crash Car Part Images into Technical Reports

Car-crash safety assessments require experts to analyze and document numerous vehicle components from various angles, resulting in a large number of post-crash images. Currently, this process relies on manual image classification and integration into structured reports — a time-consuming and error-prone workflow that limits scalability and consistency. In this paper, we present \textit{Crash2DocAI}, a tool designed to automate the classification and integration of post-crash car part images into technical reports. Our system leverages ConvNeXt, a state-of-the-art image classification model, which achieves a top-1 accuracy of 94.4\% on a newly compiled dataset of 5,772 publicly available post-crash images spanning 32 car part categories. To enable real-time deployment on CPU-only devices, we apply structured pruning and quantization, reducing the model size from 334.3\,MB to 77.6\,MB and inference time from 342\,ms to 94\,ms per image—while preserving classification performance. To enhance the robustness of our tool, we introduce an Out-of-Model-Scope (OMS) monitor based on Mahalanobis distance, which filters images outside the target domain. This binary detector achieves a precision of 71\% and a recall of 95\%, with only a 1\% overhead on inference time. We further demonstrate the practical utility of \textit{Crash2DocAI} in real-world scenarios through a user study involving 26 automotive safety experts. The results reflect a 90\% speed-up and significantly more consistent completion times. Finally, we release the National Highway Traffic Safety Administration-Post-Crash Car Parts (NHTSA-PCCP) dataset to the research community, along with the application and evaluation materials at: \url{linkprovidedincamera-readyversion}