Cost-Effective Solution for Fallen Tree Recognition Using YOLOX Object Detection

Abstract

Tropical cyclones are the world’s most deadly natural disasters, especially causing tree death by pulling out or breaking the roots of trees, which has a great impact on the forest ecosystem and forest owners. To minimize additional damage, an efficient approach is required to identify the location and distribution information of fallen trees. Several past studies have attempted to detect fallen trees, but most studies are expensive and difficult to utilize. Therefore, the purpose of this study is to solve these problems. Using a cost-effective, high-resolution secondary camera-equipped unmanned aerial vehicle (UAV) to collect data and use this data to train a YOLOX model, an object detection algorithm that can perform accurate detection in a very short time. The solution led in this study can be utilized in all scenarios that require low-cost and high-reliability object detection results. The experimental results show that our solution detected 88% of fallen trees in the image using YOLOX. The proposed model also implemented a visualization application that displays the detection results computed by the trained model in a client-friendly way. Our solution recognizes fallen trees as images or videos and presents the analysis results as a web-based visualization.