Hotspots and Smoke Detection from Forest and Land Fires Using the YOLO Algorithm ( You Only Look Once )

Abstract

The term forest and land fires is used to refer to unplanned, controlled and unwanted fires that destroy vegetated areas and their ecosystems triggered by natural or human causes . Early detection of hotspots can reduce the risk of wider forest and land fires. The use of the Deep Learning YOLO ( You Only Look Once ) algorithm is carried out to detect fire and also the smoke it produces. This study tested in 3 ways, 1) 1341 after data augmentation (496 original data), 2) 608 after data augmentation (253 original data), and 3) 1790 after data augmentation (746 original data). Detection of fire and smoke objects in the form of design, implementation and testing resulted in the YOLOv4 framework successfully producing high confidence of up to 97% in the second test. Based on the test results in this study, it is known that the image datasets used for training data greatly affect object detection and affect the confidence value. The more diverse the shape of the object from the image datasets, the lower the confidence value obtained.