| Abstract [eng] |
As global warming gains momentum and average global temperatures are constantly rising, the number of forest fires is rising along with it, due to more frequent droughts. Forest fires can destroy entire ecosystems, permanently altering landscapes and soil structure. Rapid and accurate smoke detection is essential in order to reduce the damage caused by wildfires. There are currently several forest fire prevention methods on the market, but not all of them are accurate, fast and autonomous. This paper describes how effective YOLOv5 method can be for fire localization and evaluation tasks. This method was successfully used for solving traffic, human detection and tracking problems. In this work, the speed and accuracy of the algorithm are investigated. The dependency of the accuracy is carried out using different base models on different databases and testing equipment. An additional study is performed in order to analyze the dependence of the accuracy with different union and intersection parameters. |
