CEDAnet: Individual Tree Segmentation in Dense Orchard via Context Enhancement and Density Prior

Abstract

Individual Tree Segmentation (ITS) is a pivotal technique in orchard research, estimating tree counts and delineating crown contours. This method provides foundational data for assessing orchard health, nutritional composition, and predicting yield. Unmanned Aerial Vehicles (UAVs) have become an essential data source for Intelligent Transportation Systems (ITS) due to their capability to capture ultra-fine details. However, current DL-based ITS methods struggle to accurately handle densely overlapping fruit tree distributions with similar characteristics in UAVs images, primarily due to the intricate nature of spatial arrangements in such scenarios. In this paper, we propose CEDAnet, a context enhancement and density adjustment network, to address the challenge of dense fruit trees segmentation. Specifically, a Transformer-based Contextual Aggregation Module (TCAM) is designed to distinguish different instances and refine the boundary of the instances. We have proposed a Density-guided Non-maximum Suppression (DG-NMS) method to adaptively generate sufficient candidate bounding boxes, aiming to retain more potential instances in dense trees. To evaluate the effectiveness and robustness of our proposal, we curated two ITS datasets constructed with imagery captured by UAVs, namely iSCHID and iSMMID based on their respective spatial characteristics. Experimental results on both two datasets demonstrated that CEDAnet yields competitive results in ITS tasks, with the bounding box AP of 0.498, segmentation AP of 0.493 in iSCHID, and the bounding box AP of 0.706, segmentation AP of 0.703 in iSMMID. Both two datasets and the code of CEDAnet will be publicly available at https://github.com/Lihy256/CEDAnet.