基于改进YOLOv7的杨梅树单木检测

为了快速检测和统计杨梅树的数量，该研究提出了一种基于改进YOLOv7的杨梅树单木检测模型：YOLOv7-ACGDmix。首先，对YOLOv7的可扩展高效长程注意力网络（extended-efficient long-range attention networks, E-ELAN）进行改进，通过融合兼具卷积和注意力机制优势的ACmix（a mixed model that enjoys the benefit of both self-attention and convolution）结构得到AC-E-ELAN模块，提升模型的学习和推理能力，引入可变形卷积（deformable convolutional networks version 2, DCNv2）结构得到DCNv2-E-ELAN模块，增强模型对不同尺寸目标的提取能力；其次，采用内容感知特征重组（content-aware reassembly of features, CARAFE）上采样模块，提高模型对重要特征的提取能力；然后，在主干和头部网络部分添加全局注意力机制（global-attention mechanism, GAM），强化特征中的语义信息和位置信息，提高模型特征融合能力；最后，采用WIoU（wise intersection over union）损失函数减少因正负样本数据不平衡造成的干扰，增强模型的泛化性。在公开数据集上的试验结果表明，YOLOv7-ACGDmix模型的精确率达到89.1%，召回率达到89.0%，平均精度均值（mean average precision, mAP）达到95.1%，F₁-score达到89.0%，相比于原YOLOv7模型分别提高1.8、4.0、2.3和3.0个百分点。与Faster R-CNN、SSD、YOLOv8模型相比，改进模型的平均精度均值（mAP_0.5）分别提高了9.8、2.2、0.7个百分点。实地采集杨梅树样本数据的检测精确率87.3%、召回率85.7%。试验表明，改进模型为基于无人机影像的杨梅树单木检测提供了一种有效的解决方案，对果园精准管理的发展具有重要意义。

Detection of individual trees of bayberry using improved YOLOv7

Individual tree detection of bayberry trees can greatly contribute to the precise management, accurate prediction of yield, scientific irrigation and pest control. However, most of the commonly-used traditional techniques (such as remote sensing images) are limited to the digital development of orchards at present, due to the cumbersome process and low efficiency. This study aims to rapidly detect and count the number of bayberry trees using deep learning. An improved YOLOv7-ACGDmix model was proposed to detect the individual tree using the You Only Look Once version 7 (YOLOv7) model. Firstly, the Extended-Efficient Long-Range Attention Networks module of YOLOv7 was improved to integrate the mixed model. Both convolution and attention mechanisms modules (AC-E-ELAN, self-Attention and Convolution extended-efficient long-range attention networks) were obtained to enhance the learning and reasoning of the original model. The accuracy of model was achieved to recognize the bayberry tree individuals with blurred or heavily occluded boundaries in dense scenes; Secondly, the deformable convolutional networks version 2 was added into the E-ELAN (extended-efficient long-range attention networks) module. The DCNv2-E-ElAN (deformable convolutional networks version 2 extended-efficient long-range attention networks) module was then obtained to detect the features of different sizes, especially small features and intensive situations; Thirdly, the Content-Aware ReAssembly of Features upsampling operator was used to reduce the feature information loss of the input image in the network sensory field; Fourthly, the global attention mechanism was introduced to reduce the interference of complex background on the model. The reasonable filling was realized to correct the feature information, in order to improve the detection performance of the model; Finally, the Wise Intersection over Union loss function was used to reduce the competitiveness of the high-quality anchor box and the harmful gradient that generated by low-quality samples. The overall performance of the neural network was improved after that. A series of experiments were conducted on an open dataset. A total of 611 images were divided into a training set, validation set, and test set, according to 8:1:1. The test set was further divided into simple, complex and special scenes to test the robustness and generalization of the improved model. A comparison was then made with the mainstream models, respectively. The experimental results showed that the YOLOv7-ACGDmix model was significantly improved the leakage detection, compared with the original network. The precision rate, the recall rate, the mean average precision, and the F₁-score were 89.1%, 89.0%, 95.1% and 89.0%, respectively, which were 1.8, 4.0, 2.3, and 3.0 percentage points higher than the original YOLOv7 model, respectively. The mean average precision of improved model was improved by 9.8, 2.2, 0.7, and 2.3 percentage points, compared with the Faster R-CNN, SSD, YOLOv8, and the original YOLOv7 model, respectively. Finally, a field-collected dataset was tested to verify the performance. In summary, this finding can provide an effective solution to detect the individual bayberry trees using unmanned aerial vehicle imagery in the precision management of orchards.