@article{YIN2026, 
author = {Shanglong YIN and Dawu LI},
title = {Research Advances in Striated Mark Analysis for Skeletal Trauma Documentation and Identification},
year = {2026},
journal = {Chinese Journal of Forensic Sciences},
volume = {2026},
number = {1},
pages = {32-40},
keywords = {tool mark, striated mark, skeletal trauma, mark recovery technology, image recognition technology},
url = {https://www.sciopen.com/article/10.3969/j.issn.1671-2072.2026.01.004},
doi = {10.3969/j.issn.1671-2072.2026.01.004},
abstract = {The collection, recognition and identification of tool marks on skeletal trauma at crime scenes remain critical challenges in criminal investigation. As an important subset of tool marks, striated traces have garnered substantial attention due to their high prevalence in criminal activities. This paper reviews the current research status regarding methods for collecting and identifying striated traces resulting from skeletal trauma. Firstly, it delineates the application status and limitations of two-dimensional (2D) and three-dimensional (3D) acquisition technologies within this domain. Subsequently, it systematically evaluates recognition techniques for striated traces based on algorithms and deep learning, with a specific focus on the applicable objects and scope of image recognition technology. Finally, based on existing research, this paper provides a summary and prospects for future research directions.}
}