Integration of Object Detection in Crime Scene Investigation
Keywords:
YOLOv8, Object Detection, Crime Scene Investigation, Forensic Science, Custom DatasetAbstract
Object detection is applicable in various fields, encompassing Crime Scene Investigation. The act of capturing evidence through photography in crime scenes is of paramount importance. Concurrently, object detection and scene analysis can be integrated by the investigator during this process. Nonetheless, the investigative procedure involves multiple phases. In this research, object detection is employed to identify crucial evidence discovered at crime scenes, along with objects commonly linked to assault cases as statistics reported by the National Institute of Justice (NIJ) in the United States of America. Challenges such as pen guns and illegal firearms persist in criminal activities, although they are not no category of legal firearms. Transfer learning techniques are adopted in this study from an existing model, utilizing pre-trained models to identify crucial evidence in crime scenes. The experiment gathered 494 images of pen guns, illegal firearms, and associated objects from online police news reports to form a custom dataset. The process of annotating, training, evaluating, and fine-tuning the custom dataset led to experimental outcomes demonstrating a high Mean Average Precision (mAP) across all target’s custom datasets. The model reached convergence at approximately epoch 100, achieving high precision as 0.97, recall as 0.926 and box mAP50 of as 0.974 Additionally, box mAP50-95 as 0.817 However, this paper presents a confusion matrix of customized specific classes with a low volume dataset. These findings highlight the potential application of object detection in crime scene investigations with the aid of object detection. Consequently, this approach could aid in formulating a project blueprint for a Crime Scene Investigation model and furthering the detection of evidence objects in the future with very large volume of dataset. In conclusion, this study illustrates the capability of detecting pen guns, illegal firearms, and related objects through object detection techniques.
References
W. J. Chisum, B. E. Turvey, and J. Freeman, Crime Scene Investigation. Elsevier, 2011. doi: 10.1016/B978-0-12-386460-4.00007-2.
“Forensic Photography.” 2023. doi: 10.1002/9781119763406.ch13.
A. Neale, “Photographing Crime Scenes in 20th-Century London: Microhistories of Domestic Murder,” Twentieth Century British History, vol. 34, no. 2, pp. 399–401, 2023, doi: 10.1093/tcbh/hwac046.
N. Mendis, “Forensic Photography: Concepts and Applications for better crime scene examination,” Medico-Legal Journal of Sri Lanka, vol. 9, p. 47, Apr. 2021, doi: 10.4038/mljsl.v9i1.7429.
N. Dawnay and K. Sheppard, “From crime scene to courtroom: A review of the current bioanalytical evidence workflows used in rape and sexual assault investigations in the United Kingdom,” Science & Justice, vol. 63, no. 2, pp. 206–228, 2023, doi: https://doi.org/10.1016/j.scijus.2022.12.006.
S. Saikia, E. Fidalgo, E. Alegre, and L. Fernández-Robles, “Object Detection for Crime Scene Evidence Analysis Using Deep Learning,” in Image Analysis and Processing - ICIAP 2017 , S. Battiato, G. Gallo, and F. Schettini Raimondo and Stanco, Eds., Cham: Springer International Publishing, 2017, pp. 14–24.
S. Narejo, B. Pandey, D. Esenarro Vargas, C. Rodriguez, and M. Anjum, “Weapon Detection Using YOLO V3 for Smart Surveillance System,” Math Probl Eng, vol. 2021, pp. 1–9, 2021.
S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” CoRR, vol. abs/1506.01497, 2015.
S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. N. Asghar, and B. Lee, “A Survey of Modern Deep Learning based Object Detection Models,” CoRR, vol. abs/2104.11892, 2021.
V. Mandalapu, L. Elluri, P. Vyas, and N. Roy, “Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions,” IEEE Access, vol. 11, pp. 60153–60170, 2023, doi: 10.1109/ACCESS.2023.3286344.
J. Abraham, R. Ng, M. Morelato, M. Tahtouh, and C. Roux, “Automatically classifying crime scene images using machine learning methodologies,” Forensic Science International: Digital Investigation, vol. 39, p. 301273, 2021, doi: https://doi.org/10.1016/j.fsidi.2021.301273.
M. Nakib, R. T. Khan, Md. S. Hasan, and J. Uddin, “Crime Scene Prediction by Detecting Threatening Objects Using Convolutional Neural Network,” in 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), 2018, pp. 1–4. doi: 10.1109/IC4ME2.2018.8465583.
DaSCI, “Weapon Detection.” Universidad de Jaen and Universidad de Cordoba, 2023. [Online]. Available: https://dasci.es/transferencia/open-data/24705/
U. Handalage and L. Kuganandamurthy, “Real-Time Object Detection Using YOLO: A Review.” Dec. 2021. doi: 10.13140/RG.2.2.24367.66723.
ultralytics, “Tips for Best Training Results.” Apr. 2023. [Online]. Available: https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/
NIJ, “Evidence Analysis and Processing.” 2023. [Online]. Available: https://nij.ojp.gov/topics/forensics/evidence-analysis-and-processing
NIJ, “Biological Evidence.” 2023. [Online]. Available: https://projects.nfstc.org/property_crimes/module01/pro_m01_t04.htm
NIJ, “Overview of Trace Evidence.” 2023. [Online]. Available: https://nij.ojp.gov/topics/articles/overview-trace-evidence
NIJ, “Digital Evidence and Forensics.” 2023. [Online]. Available: https://nij.ojp.gov/digital-evidence-and-forensics
J. Peterson, I. J. Sommers, D. Baskin, and D. Johnson, “The Role and Impact of Forensic Evidence in the Criminal Justice Process.” National Institute of Justice, 2010.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001, pp. I–I.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, pp. 886–893 vol. 1.
D. G. Lowe, “Object recognition from local scale-invariant features,” in Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, pp. 1150–1157 vol.2.
Z. Cai and N. Vasconcelos, “Cascade R-CNN: Delving into High Quality Object Detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6154–6162.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
W. Liu, D. Anguelov, D. Erhan, S. Szegedy Christian and Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European conference on computer vision, 2016, pp. 21–37.
J. Solawetz and Francesco, “What is YOLOv8? The Ultimate Guide,” Roboflow Blog, Apr. 2023, [Online]. Available: https://blog.roboflow.com/whats-new-in-yolov8/
Ultralytics, “Ultralytics YOLOv8 Docs.” 2023. [Online]. Available: https://docs.ultralytics.com/
R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. da Silva, “A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit,” Electronics (Basel), vol. 10, no. 3, p. 279, 2021.
G. Boesch, “YOLOv8: The Fastest Object Detection Algorithm (2024 Guide),” viso.ai, 2024, [Online]. Available: https://viso.ai/deep-learning/yolov8-guide/
J. Agrawal, “Mean Average Precision (mAP) Explained in Object Detection,” Medium, Nov. 2022, [Online]. Available: https://medium.com/@jalajagr/mean-average-precision-map-explained-in-object-detection-fb61adf67ef4
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
