基于无人机航拍图像与改进IntegrateNet的稠密云杉计数

苗木数量统计和库存管理对于大型苗圃经营和管理十分重要。该研究针对种植稠密的云杉地块，以无人机航拍云杉图像为对象，提出一种改进IntegrateNet模型，实现稠密云杉的准确计数。选择对稠密目标识别性能好的IntegrateNet为基础模型，根据稠密云杉粘连严重以及杂草背景干扰进行改进，首先使用自校正卷积（self-calibrated convolutions，SCConv）提高卷积感受野，增强模型对于不同尺寸云杉的适应能力。其次，在特征融合处应用十字交叉注意力机制（criss-cross attention，CCA）提高模型对上下文信息的提取能力。以平均计数准确率(mean counting accuracy，MCA)、平均绝对误差（mean absolute error，MAE）、均方根误差（root mean square error，RMSE）和决定系数R²为评价指标。分析结果表明，改进IntegrateNet模型在181幅测试集上的平均计数准确率，平均绝对误差，均方根误差，决定系数分别达到98.32%，8.99株、13.79株和0.99，相较于TasselNetv3_lite、TasselNetv3_seg和IntegrateNet模型，平均计数准确率分别提升16.44、10.55和9.26个百分点，平均绝对误差分别降低25.62、10.45和6.99株，均方根误差分别降低48.25、13.84和12.52株。改进IntegrateNet模型能够有效提高稠密云杉的计数准确率，可为完善苗木数量统计系统提供算法基础。

Dense spruce counting based on UAV aerial images and improved IntegrateNet

The number of trees is essential information for modern forestry management, which affects managers'' formulation of development strategies. At present, tree inventory mainly relies on manual counting, which is costly, time-consuming and labor-intensive. Using unmanned aerial vehicle (UAV) to count targets has become increasingly popular in agriculture and forestry due to its low cost, ease of operation, and flexibility of use. This study focused on using UAV images to count spruce numbers. A total of 603 images with an average canopy density of 81% and an average plant density of 6667 plants/hm² were selected for the dataset, each containing an average of 354 spruce trees. Among these, 205 images had interference factors such as weeds. The images were divided into a training set and a test set at a ratio of 7:3. The training set was expanded by randomly flipping the images to improve the robustness to different flight attitudes of UAV. After data augmentation, 844 training images and 181 test images were obtained. Aiming at the problem of dense spruces in natural environments including severe adhesion and background interference such as weeds with similar characteristics to spruces, we selected IntegrateNet, a model known for its strong performance in dense target counting tasks as baseline model for spruces. This study then worked to improve the IntegrateNet model to achieve a more accurate counting of dense spruce that is closer to real-world conditions. First of all, this paper used the self-calibrated convolutions (SCConv) to replace the ordinary convolutional layers at 1/8, 1/16 feature maps and density maps in the baseline model to expand the convolution receptive field. Secondly, in order to deal with background interference problems such as weeds and the serious adhesion of the target, this paper added the criss-cross attention mechanism (CCA) to the feature fusion of the IntegrateNet model. It can consider the horizontal and vertical context information of each pixel to generate richer semantic features to improve the contextual information extraction ability of the model. The mean counting accuracy(MCA), mean absolute error(MAE), root mean square error(RMSE), and the coefficient of determination R² are used as evaluation indicators. The ablation experiments and comparative experiments are designed to verify the performance of the proposed model. The ablation experiments show that the improved methods proposed in this paper can effectively improve the counting accuracy of the model. The comparative experiments show that the improved IntegrateNet model proposed in this study has MCA, MAE and RMSE reached 98.32%, 8.99 plants and 13.79 plants respectively, and the R² was 0.99.Compared with TasselNetv3_lite, TasselNetv3_seg, and IntegrateNet models, the MCA of the improved IntegrateNet model increased by 16.44, 10.55, and 9.26 percentage points, the MAE of the improved IntegrateNet model decreased by 25.62, 10.45, and 6.99 plants, and the RMSE of the improved IntegrateNet model decreased by 48.25, 13.84, and 12.52 plants. In summary, the improved IntegrateNet model proposed in this study demonstrated a significant increase in accuracy for counting dense spruce in natural environments. Moreover, our research is not limited to spruce, as the methodology can be applied to other tree species as well. It can providing technical support for subsequent forestry intelligent statistics.