Enhancing Bloodstain Analysis Through AI-Based Segmentation: Leveraging Segment Anything Model for Crime Scene Investigation
It addresses labor-intensive segmentation in criminology, but is incremental as it adapts an existing AI model to a new domain.
This paper tackles bloodstain image segmentation for crime scene investigation by applying the Segment Anything Model (SAM), finding that fine-tuned SAM improves accuracy by 2.2% and speeds up recognition by 4.70% compared to pre-trained SAM.
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}