project.pptx
- 1. 1ST INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING AND SCIENCE(ICITES)2022 ORGANIZED BY SCHOOL OF MECHANICAL SCIENCE BANNARI AMMAN INSTITUTE OF TECHNOLOGY FOREST FIRE DETECTION PAPER ID: MS311 PRESENYRD BY: Name: SARAVANA D, VIJAYA PRATHAP P, VINOTH V Reg. No: 191EE208 , 191EE247 , 191EE250 Dept: Electrical and Electronics Engineering
- 2. Problem Statement (ABSTRACT) • Forest fire caused by heat generated in litter and other biomes in summer through carelessness of people. • The most popular methods for detecting smoke from fires are typically based on air, temperature. So there is possible for false alarm. • There is no warning triggered unless the Particles enter the sensors and turn them on. • The sensors get damaged when they place near the fire.SO we come up with the image processing technique to detect the fire with help of color models.
- 3. S7 PROJECT WORK I - SECOND REVIEW Literature Survey PAPER TITLE AUTHOR DESCRIPTION A Literature Study on Image Processing for Forest Fire Detection PRIYADARSHINI M HANAMARADDI M.Tech-Student Computer Science and Engineering, R V College of Engineering, Bangalore, Karnataka, India. Forests can purify water, stabilize soil, cycle nutrients, moderate climate, and store carbon. They can create habitat for wildlife and nurture environments rich in biological diversity. They can also contribute billions of dollars to the country’s economic wealth. However, hundreds of millions of hectares of forests are unfortunately devastated by forest fire each year. Forest fire has been constantly threatening to ecological systems, infrastructure, and public safety. In the image processing based forest fire detection using YCbCr colour model, method adopts rule based colour model due to its less complexity and effectiveness. YCbCr colour space effectively separates luminance from chrominance compared to other colour spaces like RGB. The method not only separates fire flame pixels but also separates high temperature fire centre pixels by taking in to account of statistical parameters of fire image in YCbCr colour space like mean and standard deviation. This paper presents a literature study on Image processing for forest fire detection. S NO 1.
- 4. PAPER TITLE AUTHOR DESCRIPTION Image Processing Based Forest Fire Detection Article in INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING & TECHNOLOGY · February 2012 Vipin Venugopal (National Institute of Technology-Puducherry.) A novel approach for forest fire detection using image processing technique is proposed. A rule based color model for fire pixel classification is used. The proposed algorithm uses RGB and YCbCr color space. The advantage of using YCbCr color space is that it can separate the luminance from the chrominance more effectively than RGB color space. The performance of the proposed algorithm is tested on two Sets of images, one of which contains fire; the other contains fire-like regions. Standard methods are used for calculating the performance of the algorithm. The proposed method has both higher detection rate and lower false alarm rate. Since,the algorithm is cheap in computation it can be used for real time forest fire detection. S NO 2.
- 5. Aim To prevent forest from wild fire and save the Environment, For the growth of nation and to save our resources from forest. Scope • To minimize Pollution. • To Save Forest. • To Prevent wild animals from extinction.
- 6. ALGORITHM USED: • The Algorithm used is CONVOLUTION NEURAL NETWORK(CNN) • Considering the limitations of traditional hand-engineering methods, we extensively studied deep learning (DL) architectures for this problem and propose a cost-effective CNN framework for flame detection. • The proposed framework balances the fire detection accuracy and computational complexity as well as reduces the number of false warnings compared to state-of- the-art fire detection schemes. Hence, our scheme is more suitable for early flame detection during surveillance to avoid huge fire disasters.
- 7. Methodology: DATA COLLECTION TRAINING A MODEL CLASSIFICATION AND PREDICTION OF FIRE
- 8. WORKING:
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- 11. THANK YOU