TY - GEN
T1 - An Effective Real-Time Surveillance System for Fire and Smoke Detection Using CNN
AU - Niranjan,
AU - Natesha, B. V.
AU - Rashmi, M.
AU - Guddeti, Ram Mohana Reddy
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Fire disasters are the most dangerous and lethal events that can cause social, economic, life losses. Early detection of fire or smoke is crucial to facilitate intervention in time to avoid large-scale damage. Hence, the effective utilization of embedded devices enhances the performance of the overall surveillance system. We proposed an efficient, low-cost, real-time, memory-optimized method for surveillance systems using the YOLOv4-tiny model for early fire and smoke detection. The proposed method provides the implementation of fire and smoke detection systems for real-world applications where the model could run on low-cost hardware like 1.44 GHz processor devices. The experimental results show that the developed surveillance system can detect fire and smoke in real-time.
AB - Fire disasters are the most dangerous and lethal events that can cause social, economic, life losses. Early detection of fire or smoke is crucial to facilitate intervention in time to avoid large-scale damage. Hence, the effective utilization of embedded devices enhances the performance of the overall surveillance system. We proposed an efficient, low-cost, real-time, memory-optimized method for surveillance systems using the YOLOv4-tiny model for early fire and smoke detection. The proposed method provides the implementation of fire and smoke detection systems for real-world applications where the model could run on low-cost hardware like 1.44 GHz processor devices. The experimental results show that the developed surveillance system can detect fire and smoke in real-time.
UR - https://www.scopus.com/pages/publications/85200669496
UR - https://www.scopus.com/pages/publications/85200669496#tab=citedBy
U2 - 10.1007/978-3-031-12700-7_49
DO - 10.1007/978-3-031-12700-7_49
M3 - Conference contribution
AN - SCOPUS:85200669496
SN - 9783031126994
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 479
EP - 487
BT - Pattern Recognition and Machine Intelligence - 9th International Conference, PReMI 2021, Proceedings
A2 - Ghosh, Ashish
A2 - Bhattacharyya, Malay
A2 - Sankar Ray, Shubhra
A2 - K. Pal, Sankar
A2 - King, Irwin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2021
Y2 - 15 December 2021 through 18 December 2021
ER -