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
https://github.com/Zdong104/Bloodstain_Analysis_Ai_Tool

通过应用预训练的 Segment Anything Model (SAM) 和微调后的 SAM 在具有多样化图像背景的血斑图像分割任务中，实验结果表明，两者均能以令人满意的准确性和效率执行血斑图像分割任务，而微调后的 SAM 在准确性上提高了总体 2.2％，在图像识别速度方面提高了 4.70％。对影响血斑识别的因素进行了分析。该研究展示了 Segment Anything Model (SAM) 在血斑图像分割中的潜在应用，证明了人工智能在刑事学研究中的有效性。