基于改进YOLO的玉米幼苗株数获取方法

为快速准确获取玉米幼苗株数、评估播种质量、进行查缺补苗等管理,对YOLO算法进行改进,提出了一种基于特征增强机制的幼苗获取检测模型(FE-YOLO),实现了对玉米幼苗株数的快速获取.该方法根据玉米幼苗目标尺寸和空间纹理特征,构建了基于动态激活的轻量特征提取网络,融合了多感受野和空间注意力机制.实验表明:FE-YOLO模...

Detection Method of Maize Seedlings Number Based on Improved YOLO

The number of maize seedlings is the essential information for sowing quality assessment. It is important to obtain the number of maize seedlings quickly and precisely for investigation and filling the gaps with seedlings. To improve the real time and precision of the acquisition of maize seedling number, the YOLO model (FE-YOLO) was improved, and the detection and acquisition of maize seedling number were realized. Firstly, dynamic ReLU was used to improve the bottleneck layer of MobileNet and the feature extraction performance of MobileNet was increased. Then, according to the target size and spatial texture characteristics of maize seedlings, the multi-receptive field fusion and spatial attention mechanism were used to enhance the feature expression. The experimental results showed that the FE-YOLO model enhanced the spatial texture characteristics of the seedlings, reduced the complexity of the model, made the mAP and recall rates reach 87.22% and 91.54%, respectively, and the floating-point operations per second and detection consumption time were only 7.91% and 33.76% of YOLO v3. FE-YOLO can detect the maize seedlings in the UAV orthoimage, and then Equation (13) was used to estimate the planting density. FE-YOLO had low complexity and high recognition accuracy, which can provide support for maize seedling management.