小麦穗数对小麦产量有重要影响,小麦穗数的快速准确检测对小麦估产和粮食安全具有重要意义。计算机视觉和机器学习已被广泛研究作为人类检测的潜在替代方案。然而,具有高精度的模型计算密集且耗时,和轻量级模型往往具有较低的精度。为了解决这些问题,选择YOLO-FastestV2作为基础模型,对小麦捆检测进行综合研究分析。在这项研究中,我们构建了一个小麦目标检测数据集,包括11,451张图像和496,974个边界框。本研究的数据集是基于全球小麦检测数据集和小麦捆检测数据集构建的,由PP飞桨出版。我们选择了三种关注机制,大型可分离内核注意力(LSKA),高效渠道注意力(ECA)和有效的多尺度注意力(EMA),增强骨干网的特征提取能力,提高底层模型的精度。首先,注意机制是在骨干网的基础阶段和输出阶段之后添加的。第二,选择在基础和输出阶段之后进一步提高模型准确性的注意力机制来构建具有两阶段附加注意力机制的模型。另一方面,通过引入SimConv对LightFPN模块进行改进,构建了SimLightFPN模型以提高模型精度。研究结果表明,YOLO-FastestV2-SimLightFPN-ECA-EMA混合模型,它在基础阶段引入了ECA注意力机制,并在输出阶段引入了EMA注意力机制和SimLightFPN模块的组合,具有最佳的整体性能。模型的准确率为P=83.91%,R=78.35%,AP=81.52%,F1=81.03%,在总体评估中,它在GPI中排名第一(0.84)。该研究检查了在资源有限的设备上部署麦穗检测和计数模型,为农业自动化和精准农业的发展提供新的解决方案。
The number of wheat spikes has an important influence on wheat yield, and the rapid and accurate detection of wheat spike numbers is of great significance for wheat yield estimation and food security. Computer vision and machine learning have been widely studied as potential alternatives to human detection. However, models with high accuracy are computationally intensive and time consuming, and lightweight models tend to have lower precision. To address these concerns, YOLO-FastestV2 was selected as the base model for the comprehensive study and analysis of wheat sheaf detection. In this study, we constructed a wheat target detection dataset comprising 11,451 images and 496,974 bounding boxes. The dataset for this study was constructed based on the Global Wheat Detection Dataset and the Wheat Sheaf Detection Dataset, which was published by PP Flying Paddle. We selected three attention mechanisms, Large Separable Kernel Attention (LSKA), Efficient Channel Attention (ECA), and Efficient Multi-Scale Attention (EMA), to enhance the feature extraction capability of the backbone network and improve the accuracy of the underlying model. First, the attention mechanism was added after the base and output phases of the backbone network. Second, the attention mechanism that further improved the model accuracy after the base and output phases was selected to construct the model with a two-phase added attention mechanism. On the other hand, we constructed SimLightFPN to improve the model accuracy by introducing SimConv to improve the LightFPN module. The results of the study showed that the YOLO-FastestV2-SimLightFPN-ECA-EMA hybrid model, which incorporates the ECA attention mechanism in the base stage and introduces the EMA attention mechanism and the combination of SimLightFPN modules in the output stage, has the best overall performance. The accuracy of the model was P=83.91%, R=78.35%, AP= 81.52%, and F1 = 81.03%, and it ranked first in the GPI (0.84) in the overall evaluation. The research examines the deployment of wheat ear detection and counting models on devices with constrained resources, delivering novel solutions for the evolution of agricultural automation and precision agriculture.