TY - GEN
T1 - AFAR-YOLO
T2 - 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024
AU - Irham, Ainal
AU - Kurniadi,
AU - Yuliandari, Khoirinisa
AU - Fahreza, Farhan Mozart Aditya
AU - Riyadi, Daffa
AU - Shiddiqi, Ary Mazharuddin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study focuses on developing an advanced early warning system utilizing YOLOv5 to detect objects indicative of potential fire hazards. This research is motivated by the fact that continuous monitoring is impractical, especially in high-risk and inaccessible areas. We introduce an innovative approach: adaptive YOLO for object detection to enhance early fire detection capabilities in these challenging environments. The main contribution of this research is the development of adaptive frames per second (FPS) resolution in YOLO object detection. We found that implementing adaptive FPS alone does not significantly impact the efficiency of CPU and RAM resources in the tested devices. However, when adaptive FPS is combined with adaptive resolution, resource usage is significantly reduced-specifically, a 33% decrease in CPU usage and a 0.5-1% (200-400 MB) reduction in RAM usage. These efficiency gains are important in enhancing safety in the industrial sector.
AB - This study focuses on developing an advanced early warning system utilizing YOLOv5 to detect objects indicative of potential fire hazards. This research is motivated by the fact that continuous monitoring is impractical, especially in high-risk and inaccessible areas. We introduce an innovative approach: adaptive YOLO for object detection to enhance early fire detection capabilities in these challenging environments. The main contribution of this research is the development of adaptive frames per second (FPS) resolution in YOLO object detection. We found that implementing adaptive FPS alone does not significantly impact the efficiency of CPU and RAM resources in the tested devices. However, when adaptive FPS is combined with adaptive resolution, resource usage is significantly reduced-specifically, a 33% decrease in CPU usage and a 0.5-1% (200-400 MB) reduction in RAM usage. These efficiency gains are important in enhancing safety in the industrial sector.
KW - Adaptive FPS
KW - Adaptive Object Detection
KW - Adaptive Resolution
KW - Adaptive YOLO
KW - Object Detection
UR - http://www.scopus.com/inward/record.url?scp=85190543771&partnerID=8YFLogxK
U2 - 10.1109/ICETSIS61505.2024.10459422
DO - 10.1109/ICETSIS61505.2024.10459422
M3 - Conference contribution
AN - SCOPUS:85190543771
T3 - 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024
SP - 594
EP - 598
BT - 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 January 2024 through 29 January 2024
ER -
