Tree counting plays an important role in wide applications of environmental protection, agricultural planning and crop yield estimation. However, traditional tree counting methods require expensive feature engineering, which causes additional mistake and cannot be optimized overall. Recently, deep learning based approaches have been adopted for this task which demonstrate state-of-the-art performance. In this paper, a point-wise supervised segmentation network is proposed based on a deep segmentation network with only weak supervision, which can complete localization and generate mask of each tree simultaneously. In the first step, a tree feature extractor module is adopted to extract features of input images with a novel encoder–decoder network. In the second step, an effective strategy is designed to deal with different conditions with mask predictions. Finally, the basic localization and rectification guidance are introduced to train the whole network. In addition, two different datasets are created and an existing challenging plant dataset is selected to evaluate the proposed method. Experimental results on those datasets show that the proposed method outperforms the state-of-the-art methods in most challenging conditions. This method has great potential to reduce human labor due to effective automatic generated masks.

树木计数在环境保护，农业计划和作物产量估算的广泛应用中起着重要作用。但是，传统的树计数方法需要昂贵的特征工程，这会导致其他错误，并且无法进行整体优化。最近，针对此任务采用了基于深度学习的方法，这些方法展示了最新的性能。本文提出了一种基于仅需弱监督的深度分割网络的逐点监督分割网络，可以完成定位并同时生成每棵树的掩码。第一步，采用树特征提取器模块通过新颖的编码器-解码器网络提取输入图像的特征。第二步 设计一种有效的策略来利用蒙版预测来应对不同的条件。最后，介绍了基本的本地化和整改指导来训练整个网络。此外，创建了两个不同的数据集，并选择了现有的具有挑战性的植物数据集来评估所提出的方法。在这些数据集上的实验结果表明，在最具挑战性的条件下，该方法优于最新方法。由于有效地自动产生了口罩，这种方法具有减少人力的巨大潜力。在这些数据集上的实验结果表明，在最具挑战性的条件下，该方法优于最新方法。由于有效地自动产生了口罩，这种方法具有减少人力的巨大潜力。在这些数据集上的实验结果表明，在最具挑战性的条件下，该方法优于最新方法。由于有效地自动产生了口罩，这种方法具有减少人力的巨大潜力。