Forest Fire Detection Using Deep Learning Techniques
Dr.A.H.Nandhu Kishore*1, Dharani L.R1, Haifa Fathima H1, Srinidhi Priya V1, Nivashini K.K1
1 Students - Department of Computer Science Engineering
*1 Associate Professor, Department of Computer Science Engineering
PSNA College of Engineering and Technology, Dindigul - 624005, Tamil Nadu, India
Abstract - Forest-fires are real threats to human lives, environmental systems and infrastructure. It is predicted that forest fires could destroy half of the world’s forests by the year 2030. The only efficient way to minimize the forest fires damage is adopt early fire detection mechanisms. Thus, forest-fire detection systems are gaining a lot of attention on several research centres and universities around the world. Currently, there exists many commercial fire detection sensor systems, but all of them are difficult to apply in big open areas like forests, due to their delay in response, necessary maintenance, high cost and other problems. In this study, image processing based has been used due to several reasons such as quick development of digital cameras technology, the camera can cover large areas with excellent results, the response time of image processing methods is better than that of the existing sensor systems, and the overall cost of the image processing systems is lower than sensor systems. Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the same features with fire, which may result in high false alarms rate. This project presents a new video-based, image processing forest fires detection method, which consists of four stages. First, a background-subtraction algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using RGB colour space. Thirdly, features extraction is used to differentiate between actual fire and fire-like objects, because candidate regions may contain moving fire-like objects. Finally, convolutional neural network algorithm is used to classify the region of interest to either real fire or non-fire. The final experimental results verify that the proposed method effectively identifies the forest fires.