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 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 334.3MB to 77.6MB and inference time from 342ms to 94ms 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 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, together with the application and evaluation materials. https://gitlab.com/divisvaclav/crash2docai