AI based forest fire detection using deep learning techniques

Forest fires pose serious risks to ecological components, infrastructure, and human life. It is projected that half of the forest ecosystems would have been completely destroyed by wildfires by 2030. Reducing the damage caused by forest fires can only be accomplished by using fire detection devices. As a result, study centers and universities all around the world are paying close attention to forest-fire detection techniques. There are already several different commercial automated fire sensor systems available, but they are all difficult to use in wide open regions like woodlands because of their poor reaction times, high maintenance needs, and other problems. Image processing has been used in this study due to the quick development of digital camera technology, the camera's excellent coverage of wide areas, the faster response time of image processing methods compared to established sensor systems, and the general lower cost of image processing systems compared to sensor systems. False alarm rates may be substantial since certain objects have characteristics with forest fires, which make reliable forest fire detection systems challenging. This study presents a revolutionary four-stage video-based image processing approach for forest fire detection. First, a background-subtraction approach is applied to identify moving zones. Second, potential fire zones are located using RGB colour space. Thirdly, features extraction is used to differentiate between actual fire and fire-like objects since candidate zones may contain moving fire-like objects. Finally, the convolutional neural network method is used to classify the region of interest as either a genuine fire or not. The results of the last experiment show that the recommended strategy finds forest fires with success.