Enhancing Bloodstain Analysis Through AI-Based Segmentation: Leveraging
  Segment Anything Model for Crime Scene Investigation
Zihan Dong ZhengDong Zhang
Bloodstain pattern analysis plays a crucial role in crime scene
investigations by providing valuable information through the study of unique
blood patterns. Conventional image analysis methods, like Thresholding and
Contrast, impose stringent requirements on the image background and is
labor-intensive in the context of droplet image segmentation. The Segment
Anything Model (SAM), a recently proposed method for extensive image
recognition, is yet to be adequately assessed for its accuracy and efficiency
on bloodstain image segmentation. This paper explores the application of
pre-trained SAM and fine-tuned SAM on bloodstain image segmentation with
diverse image backgrounds. Experiment results indicate that both pre-trained
and fine-tuned SAM perform the bloodstain image segmentation task with
satisfactory accuracy and efficiency, while fine-tuned SAM achieves an overall
2.2\% accuracy improvement than pre-trained SAM and 4.70\% acceleration in
terms of speed for image recognition. Analysis of factors that influence
bloodstain recognition is carried out. This research demonstrates the potential
application of SAM on bloodstain image segmentation, showcasing the
effectiveness of Artificial Intelligence application in criminology research.
We release all code and demos at
\url{https://github.com/Zdong104/Bloodstain_Analysis_Ai_Tool}