TY - GEN
T1 - Enforcing Social Distancing with YOLO Algorithm Utilizing Object-to-Object Distance
AU - Irsan, Muhamad
AU - Hassan, Rosilah
AU - Abdali, Taj Aldeen Naser
AU - Ishak, Mohamad Khairi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Instances of COVID-19 transmission occur daily due to individuals failing to maintain distance or engaging in physical contact with others who may be contaminated with the virus. To mitigate this issue, this study has developed a system to detect human subjects practicing social distancing. The system utilizes a Raspberry Pi 4 Model B 8GB device in combination with a Logitech HD Webcam C270 camera. To detect human subjects, the Convolutional Neural Network is employed, utilizing the You Only Look Once (YOLO) method. In the testing phase of the tool, the system successfully identifies human subjects and assesses their proximity to others. It also detects instances of social distancing violations. The system achieved an average mean Average Precision (mAP) of 0.9792, a Precision of 0.9482, a Recall of 0.9819, and an f1 score of 0.9648.
AB - Instances of COVID-19 transmission occur daily due to individuals failing to maintain distance or engaging in physical contact with others who may be contaminated with the virus. To mitigate this issue, this study has developed a system to detect human subjects practicing social distancing. The system utilizes a Raspberry Pi 4 Model B 8GB device in combination with a Logitech HD Webcam C270 camera. To detect human subjects, the Convolutional Neural Network is employed, utilizing the You Only Look Once (YOLO) method. In the testing phase of the tool, the system successfully identifies human subjects and assesses their proximity to others. It also detects instances of social distancing violations. The system achieved an average mean Average Precision (mAP) of 0.9792, a Precision of 0.9482, a Recall of 0.9819, and an f1 score of 0.9648.
KW - COVID
KW - Image
KW - Raspberry Pi
KW - Social distancing
KW - YOLO
UR - https://www.scopus.com/pages/publications/85189138580
U2 - 10.1109/ACIT58888.2023.10453716
DO - 10.1109/ACIT58888.2023.10453716
M3 - Conference contribution
AN - SCOPUS:85189138580
T3 - 2023 24th International Arab Conference on Information Technology, ACIT 2023
BT - 2023 24th International Arab Conference on Information Technology, ACIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Arab Conference on Information Technology, ACIT 2023
Y2 - 6 December 2023 through 8 December 2023
ER -