Enhancing the Quality of Ambulance Crew Work by detecting Ambulance Equipment using Computer Vision and Deep Learning
Received: 7 May 2024 | Revised: 27 May 2024 | Accepted: 29 May 2024 | Online: 9 June 2024
Corresponding author: Asmaa Alayed
Abstract
Ambulance crews play an important role in responding quickly to emergencies and rescuing patients by providing appropriate treatment. Typically, fully equipped emergency vehicles are used to transport ambulance personnel to emergency locations. The ambulance crew cleans, sterilizes, and prepares equipment after each patient transfer with great care. Additionally, they check more than 70 pieces of equipment twice a day using a checklist, which is a tedious, time-consuming, and error-prone task. This study uses computer vision and deep learning techniques to replace the manual checklist process for medical equipment to assist the crew and make the equipment availability check faster and easier. To accomplish this, a dataset containing 2099 images of medical equipment in ambulances was collected and annotated with 3000 labeled instances. An experimental study compared the performance of YOLOv9-c, YOLOv8n, and YOLOv7-tiny. YOLOv8n demonstrated the best performance with a mAP50 of 99.2% and a speed of 3.3 ms total time per image. Therefore, YOLOv8 was selected for the proposed system due to its high accuracy and detection speed, which make it suitable for mobile applications. The presence of an application integrated with computer vision and deep learning technologies in paramedic devices can assist in reviewing the equipment checklist, reducing human errors, speeding up the review process, and alleviating the burden on paramedics in their work.
Keywords:
Medical Equipment Detection, Ambulance Equipment Detection, Computer Vision, Deep Learning, You-Only-Look-Once (YOLO)Downloads
References
A. Vahab, M. S. Naik, P. G. Raikar, and S. R. Prasad, "Applications of object detection system," International Research Journal of Engineering and Technology (IRJET), vol. 6, no. 4, pp. 4186–4192, Apr. 2019.
R. Dockter, R. Sweet, and T. Kowalewski, "A fast, low-cost, computer vision approach for tracking surgical tools," in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, Sep. 2014, pp. 1984–1989.
C. Cao et al., "Deep Learning and Its Applications in Biomedicine," Genomics, Proteomics & Bioinformatics, vol. 16, no. 1, pp. 17–32, Feb. 2018.
V. A. Rajendran and S. Shanmugam, "Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12734–12739, Feb. 2024.
R. Gurumoorthy and M. Kamarasan, "Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12831–12836, Feb. 2024.
B. Chan et al., "Computer vision for object detection; machine learning-based identification of surgical equipment," Archives of Disease in Childhood, vol. 104, no. 4, Nov. 2019 https://doi.org/10.1136/
archdischild-2019-gosh.75.
B. Ran, B. Huang, S. Liang, and Y. Hou, "Surgical Instrument Detection Algorithm Based on Improved YOLOv7x," Sensors, vol. 23, no. 11, Jan. 2023, Art. no. 5037.
"Roboflow: Computer vision tools for developers and enterprises," https://roboflow.com/.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 779–788.
M. Hussain, "YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection," Machines, vol. 11, no. 7, Jul. 2023, Art. no. 677.
A. Alayed, R. Alidrisi, E. Feras, S. Aboukozzana, and A. Alomayri, "Real-Time Inspection of Fire Safety Equipment using Computer Vision and Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13290–13298, Apr. 2024.
L. Zhang, N. Xiong, X. Pan, X. Yue, P. Wu, and C. Guo, "Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery," Algorithms, vol. 16, no. 11, Nov. 2023, Art. no. 520.
S. Li, F. Chen, Z. Sun, Z. Zhu, L. Zhou, and K. Tang, "Research on YOLOv8 Object Detection Algorithm in UAV Scenarios." Mar. 01, 2024.
K. Lan, X. Jiang, X. Ding, H. Lin, and S. Chan, "High-Efficiency and High-Precision Ship Detection Algorithm Based on Improved YOLOv8n," Mathematics, vol. 12, no. 7, Jan. 2024, Art. no. 1072.
C. T. Chien, R. Y. Ju, K. Y. Chou, and J. S. Chiang, "YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images." arXiv, May 27, 2024.
C. Y. Wang, I. H. Yeh, and H. Y. M. Liao, "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information." arXiv, Feb. 28, 2024.
T. Y. Lin et al., "Microsoft COCO: Common Objects in Context," in Computer Vision – ECCV 2014, Zurich, Switzerland, Sep. 2014, pp. 740–755.
M. Abouelyazid, "Comparative Evaluation of SORT, DeepSORT, and ByteTrack for Multiple Object Tracking in Highway Videos," International Journal of Sustainable Infrastructure for Cities and Societies, vol. 8, no. 11, pp. 42–52, Nov. 2023.
L. D. Quach, K. Nguyen, A. N. Quynh, and H. T. Ngoc, "Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small Objects," Journal of Advances in Information Technology, vol. 14, no. 5, pp. 907–917, Sep. 2023.
A. Sharma, A. Z. Ansari, R. Kakulavarapu, M. H. Stensen, M. A. Riegler, and H. L. Hammer, "Predicting Cell Cleavage Timings from Time-Lapse Videos of Human Embryos," Big Data and Cognitive Computing, vol. 7, no. 2, Jun. 2023, Art. no. 91.
G. Gledec, M. Sokele, M. Horvat, and M. Mikuc, "Error Pattern Discovery in Spellchecking Using Multi-Class Confusion Matrix Analysis for the Croatian Language," Computers, vol. 13, no. 2, Feb. 2024, Art. no. 39.
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Copyright (c) 2024 Jonab Hussain, Nada Al-Masoody, Asmaa Alsuraihi, Fay Almogbel, Asmaa Alayed
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