LiDAR and Deep Learning-Based Standing Tree Detection for Firebreaks Applications

Abstract

Forest fire prevention is very important for the protection of the ecological environment, which requires effective prevention and timely suppression. The opening of the firebreaks barrier contributes significantly to forest fire prevention. The development of an artificial intelligence algorithm makes it possible for an intelligent belt opener to create the opening of the firebreak barrier. This paper introduces an innovative vision system of an intelligent belt opener to monitor the environment during the creation of the opening of the firebreak barrier. It can provide precise geometric and location information on trees through the combination of LIDAR data and deep learning methods. Four deep learning networks including PointRCNN, PointPillars, SECOND, and PV-RCNN were investigated in this paper, and we train each of the four networks using our stand tree detection dataset which is built on the KITTI point cloud dataset. Among them, the PointRCNN showed the highest detection accuracy followed by PV-RCNN and PV-RCNN. SECOND showed less detection accuracy but can detect the most targets.