TCDNet: Tree Crown Detection From UAV Optical Images Using Uncertainty-Aware One-Stage Network

Abstract

Tree crown detection plays a vital role in forestry management, resource statistics and yields forecasting. RGB high-resolution aerial images have emerged as a cost-effective source of data for tree crown detection. To address the challenges in the detection using UAV optical images, we propose a one-stage object detection network, TCDNet. First, the network provides an attention enhancement feature extraction module to enable the model to distinguish between tree crowns and their complex backgrounds. Second, an efficient loss is introduced to enable it to be aware of the overlap between adjacent trees, thus effectively avoiding misdetection. The experimental results on two publicly available datasets show that the proposed network outperforms state-of-art networks in terms of precision, recall and mean average precision.