I-CVSSDM: IoT Enabled Computer Vision Safety System for Disaster Management

Authors

  • Parameswaran Ramesh Anna University, Chennai image/svg+xml
  • Vidhya N Anna University, Chennai image/svg+xml
  • Panjavarnam B Sri Sairam Engineering College
  • Shabana Parveen M Sri Sairam Engineering College
  • Deepak Athipan A M B Anna University, Chennai image/svg+xml
  • Bhuvaneswari P T V Anna University, Chennai image/svg+xml

DOI:

https://doi.org/10.4108/eetiot.5046

Keywords:

IoT, SSD, Mobi Net, Raspberry pi, Alert System

Abstract

INTRODUCTION: Around the world, individuals experience flooding more frequently than any other natural calamity.

OBJECTIVES: The motivation behind this research is to provide an Internet of Things (IoT)-based early warning assistive system to enable monitoring of water logging levels in flood-affected areas. Further, the SSD-MobiNET V2 model is used in the developed system to detect and classify the objects that prevail in the flood zone.

METHODS: The developed research is validated in a real-time scenario. To enable this, a customized embedded module is designed and developed using the Raspberry Pi 4 model B processor. The module uses (i) a pi-camera to capture the objects and (ii) an ultrasonic sensor to measure the water level in the flood area.

RESULTS: The measured data and detected objects are periodically ported to the cloud and stored in the cloud database to enable remote monitoring and further processing.

CONCLUSION: Also, whenever the level of waterlogged exceeds the threshold, an alert is sent to the concerned authorities in the form of an SMS, a phone call, or an email.

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References

Rafael, M, Javier, G, Antonio, S.: A Real-time measurement system for long life flood monitoring and warning applications. Sensors (Basel). 2012. Vol. 12, pp. 4213-4236. DOI: https://doi.org/10.3390/s120404213

Jiuxiang, G, Zhenhua, W, Jason, K, Lianyang, M.: Recent advances in Convolutional Neural Networks. Pattern Recogniton. 2018. Vol. 77, pp. 354-377. DOI: https://doi.org/10.1016/j.patcog.2017.10.013

MathuraBai, B, Vishnu, P, Maddali, C, Devineni, S.: Object Detetcion using SSD-MobileNet. International Research Journal of Engineering and Technology. 2022. Vol. 9, pp. 2668-2771.

Xiaolong, X, Lei, Z, Stelios, S, Eleana, A.: CLOTHO: A Large-Scale Internet of Things based Crowd Evacuation Planning System for Disaster Management. IEEE Internet of Things Journal. 2018. Vol. 5, pp. 3559-3568. DOI: https://doi.org/10.1109/JIOT.2018.2818885

Lung, E, KarAnn, T, Yun, W, Wang, J.: DEWS: A Live Visual Surveillance System for Early Drowning Detection at Pool. IEEE Transactions on Circuits and Systems for Video Technology Journal.2008. Vol. 18, pp. 25-38. DOI: https://doi.org/10.1109/TCSVT.2007.913960

Ling, H, Qiang, N, Feng, Y.: Big data oriented novel background subtraction algorithm for urban surveillance systems. Big Data Mining and Analytics. 2018. Vol. 1, pp. 137-145. DOI: https://doi.org/10.26599/BDMA.2018.9020013

Mousa, M, Zhang, X, Claudel, C.:Flash Flood Detection in Urban Cities Using Ultrasonic and Infrared Sensors. IEEE Sensors Journal. 2016. Vol. 16, pp. 7204-7216. DOI: https://doi.org/10.1109/JSEN.2016.2592359

Haidong, L, Jintao, M, Hundt, C, Bertil, S.: FeatherCNN: Fast Inference Computation with TensorGEMM on ARM Architectures. IEEE Transactions on Parallel and Distributed Systems. 2020. Vol. 31, pp. 580-594. DOI: https://doi.org/10.1109/TPDS.2019.2939785

Wang, H, Wang, P, Qian, X.: MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video. IEEE Access, 2018. Vol. 6, pp. 30296-30308. DOI: https://doi.org/10.1109/ACCESS.2018.2836921

Lee, J.: Specializing CGRAs for Light-Weight Convolutional Neural Networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2022. Vol. 41, pp. 3387-3399. DOI: https://doi.org/10.1109/TCAD.2021.3123178

Parameswaran, R, Bhuvaneswari, P.: RFID Aided Intelligent Shopping Trolley with Child Care Unit. Proceedings of IEEE International Conference on RFID Technology and Applications. Delhi. India. IEEE. 2021. pp. 264-266. DOI: https://doi.org/10.1109/RFID-TA53372.2021.9617350

Gurubaran, K, Poornesh, S, Shwetha, L.: Real Time Experimental Calibration of Ultrasonic Sensor and LoRa Communication module in LoRaWAN Architecture. Proceedings of International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies. Delhi. India. IEEE. 2023. pp. 1-6. DOI: https://doi.org/10.1109/ViTECoN58111.2023.10156966

Shashank, P, Vaibhav, P, Akshar, P , Devashish, P.: Obstacle Detection Using Ultrasonic Sensor For Amphibious Surveillance Robot. IOSR Journal of Engineering. 2018. Vol. 4, pp. 28-33.

Ramesh, P, Vidhya, N, Parveen,S.: I-SOEWM: IoT Based Solar Energized Weather Monitoring System. Indian Journal of Science and Technology. 2023. Vol. 16, pp. 1505-1515. DOI: https://doi.org/10.17485/IJST/v16i20.287

Addona, F, Flavia, S, Claudia, R, Luigi, C.: Use of a Raspberry-Pi Video Camera for Coastal Flooding Vulnerability Assessment: The Case of Riccione (Italy). Water. 2022. Vol. 14, pp. 1-23. DOI: https://doi.org/10.3390/w14070999

Jan, F, Nasro, M, Saqib, S, Zafar, I, Rashad, A.: IoT-Based Solutions to Monitor Water Level, Leakage, and Motor Control for Smart Water Tanks. Water. 2022. Vol. 14, pp. 1-36. DOI: https://doi.org/10.3390/w14030309

Liu, H, Zhang, Y,Zhang, H.: Performance analysis of different DCNN models in remote sensing image object detection. Eurasip Journal of Image and Video Processing. 2022. Vol. 9, pp. 1-18. DOI: https://doi.org/10.1186/s13640-022-00586-6

Mishra, B, Kertesz, A.: The Use of MQTT in M2M and IoT Systems:A Survey. IEEE Access. 2020.Vol. 8, pp. 201071-201086. DOI: https://doi.org/10.1109/ACCESS.2020.3035849

Swaminathan, K, Veeman, S, Dhilip, T, Ezhilarasi, S.: Deep Learning and IoT Based Assistance System for Autism Spectrum Disorder People. Proceedings of 4th IEEE Middle East and North Africa COMMunications Conference. Amman. Jordan. IEEE. 2022. pp. 83-88.

Suppakhun, Y.: Flood surveillance and alert system an advance the IoT. Proceedings of IEEE Asia Pacific Conference on Circuits and Systems. Bangkok. Thailand. IEEE. 2019. pp. 325-328. DOI: https://doi.org/10.1109/APCCAS47518.2019.8953179

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Published

06-02-2024

How to Cite

[1]
P. Ramesh, V. N, P. B, S. P. M, D. A. A M B, and B. P T V, “I-CVSSDM: IoT Enabled Computer Vision Safety System for Disaster Management”, EAI Endorsed Trans IoT, vol. 10, Feb. 2024.