From structure health monitoring to forensics: adapting computer vision to support victims of violence

Abstract

This paper explores the intersection between forensic science and Structural Health Monitoring (SHM), focusing on the pivotal role of visual indicators. These indicators are crucial in both contexts -from discerning injuries on humans to identifying structural defects. We present a novel approach utilizing computer vision-based diagnostics to aid victims of violence through advanced bruise detection, thus enhancing post-trauma care. Leveraging a specialized dataset, our study confronts the challenges inherent in data preparation and organization, as well as achieving expert consensus. We modify lightweight deep learning algorithms originally developed for engineered system diagnostics for application in the medical forensics domain. This adaptation aims to detect bruise areas under varying conditions, such as differences in skin color and lighting. A key question we address is the generalizability of these methods in diverse medical bruising scenarios, a fundamental challenge shared with SHM. Our research highlights the importance of domain knowledge transfer, drawing parallels between SHM and forensic science, and underscores the potential of this interdisciplinary approach.