Open Access Open Access  Restricted Access Subscription Access

Detection of Fire with the help of Deep Learning

Rahul Sood

Abstract


Fire Detection assists with recognizing fire in the backwoods region where a few perspectives are considered. Fire location isn't that simple to distinguish when picking the elements to identify hushes up hard. It is characterizing a product which involves Python for identifying fire where it incorporates Python bundle called OpenCV which might deal with video type inputs in an effective way. As to involve a mediator for taking care of information sources, mounting live video from CCTV is simple. To identify fire from the CCTV for certain elements characterized. Variety Intensity level is utilized to recognize fire from the environmental elements of backwoods where CCTV is taken as an information. Future proceedings,decided to bring more precision by adding Sensor for heat level distinguishing proof then, at that point, by including a breeze heading sensor and can ready to recognize the navigating of fire after a few timespan for illuminating firemen to oppose fire from harming nature in a colossal way.

GoogleAPI is additionally connected where the offices of Google guides can be incorporated to send a caution to firemen about the Latitude and Longitude values to precisely recognize the region. can ready to recognize fire with more exactness by diminishing the harm of timberland assets which are being annihilated by Forest fire Disaster.


Full Text:

PDF

References


Tian, H., Li, W., Ogunbona, P. O., & Wang, L. (2017). Detection and separation of smoke from single image frames. IEEE Transactions on Image Processing, 27(3), 1164-1177..

Yin, Z., Wan, B., Yuan, F., Xia, X., & Shi, J. (2017). A deep normalization and convolutional neural network for image smoke detection. Ieee Access, 5, 18429-18438.Oluwafemi, T., Kohno, T., Gupta, S., & Patel, S. (2013). Experimental Security Analyses of {Non-Networked} Compact Fluorescent Lamps: A Case Study of Home Automation Security. In LASER 2013 (LASER 2013) (pp. 13-24).

Kesh, N. T., & Vidhyalakshimi, S. (2015). Home Automation Systems-A Study. International Journal of Computer Applications, 975, 8887.

Harmo, P., Taipalus, T., Knuuttila, J., Vallet, J., & Halme, A. (2005, August). Needs and solutions-home automation and service robots for the elderly and disabled. In 2005 IEEE/RSJ international conference on intelligent robots and systems (pp. 3201-3206). IEEE.

Pang, Z., Cheng, Y., Johansson, M. E., & Bag, G. (2014, December). Preliminary study on wireless home automation systems with both cloud-based mode and stand-alone mode. In 2014 IEEE 17th International Conference on Computational Science and Engineering (pp. 970-975). IEEE.


Refbacks

  • There are currently no refbacks.