通过局部视觉检测与全局计数估计实现种植微地点的自动计数
Automatic counting of planting microsites via local visual detection and global count estimation
AI总结：
针对人工统计种植区块土堆数量低效易错的问题，提出结合局部深度特征检测与区块级全局估计的全自动计数框架，并在UAV数据集上验证其精度与效率优势。
摘要：
在林业中，通过堆土进行机械化立地准备被广泛用于种植作业之前。规划种植作业时的主要问题之一是难以估计一个种植区块上存在的土堆数量，因为其数量可能会根据立地特征而大幅变化。这种估计通常由多名林业工人通过实地调查完成。然而，该过程容易出错且速度缓慢。受无人机（UAV）影像和人工智能最新进展的启发，我们提出了一个全自动框架，用于估计种植区块上的土堆数量。利用计算机视觉和机器学习，我们使用两个预测模型将计数任务表述为一个监督学习问题。首先使用局部检测模型基于深度特征检测可见土堆，随后应用全局预测函数，基于区块级特征提供最终估计。为了评估所提出的方法，我们构建了一个具有挑战性的 UAV 数据集，代表了若干具有不同特征的种植区块。所进行的实验证明了所提方法的鲁棒性，其在精度上优于人工方法，同时显著减少了时间和成本。
英文摘要：
In forest industry, mechanical site preparation by mounding is widely used prior to planting operations. One of the main problems when planning planting operations is the difficulty in estimating the number of mounds present on a planting block, as their number may greatly vary depending on site characteristics. This estimation is often carried out through field surveys by several forestry workers. However, this procedure is prone to error and slowness. Motivated by recent advances in UAV imagery and artificial intelligence, we propose a fully automated framework to estimate the number of mounds on a planting block. Using computer vision and machine learning, we formulate the counting task as a supervised learning problem using two prediction models. A local detection model is firstly used to detect visible mounds based on deep features, while a global prediction function is subsequently applied to provide a final estimation based on block-level features. To evaluate the proposed method, we constructed a challenging UAV dataset representing several plantation blocks with different characteristics. The performed experiments demonstrated the robustness of the proposed method, which outperforms manual methods in precision, while significantly reducing time and cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
