Title: Using deep learning and accident report analysis to determine culpability and detect car damage

Authors: Sonia Lajmi

Addresses: Department of Information Technology, Faculty of Computing and Information, Al-Baha University, Al-Baha, Alaqiq, 65779-7738, Saudi Arabia; MIRACL Laboratory, Technopole of Sfax, University of Sfax, P.O. Box 242, 3031, Sfax, Tunisia

Abstract: Conventional car bumper and driver culpability prediction rely on the experience of specialised experts. A long wait may result from obtaining such a prediction at the accident scene. It is possible to avoid this kind of wait by generating an automatic report. In this paper, a culpability estimation approach based on an analysis of accident reports and detection of damage caused in cars is presented. Our system employs a fine-tuning-based approach to enable the prediction of the kind, position, and intensity of vehicle bumps. For culpability prediction, is based on mining data from accident reports. We obtained a precision of 94.7%, a recall of 96.3%, and a mAP of 97.7%. The dataset size for car damage is 7,402 images. As for the prediction of culpability, it gave an accuracy of 91%. The dataset size for accident reports is 135,764 records.

Keywords: deep learning; fine-tuning; culpability prediction; car damage detection.

DOI: 10.1504/IJPMB.2025.147400

International Journal of Process Management and Benchmarking, 2025 Vol.20 No.4, pp.468 - 490

Received: 02 Feb 2024
Accepted: 22 Apr 2024
Published online: 15 Jul 2025 *

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