基于UANP-MT的半监督菜心杂草分割方法

杂草作为一种常见的农业问题，对农作物的生长造成比较严重的影响，控制和管理杂草是农业生产活动中的重要一环。近年来，随着无人机技术和人工智能技术的快速发展，基于无人机平台的特定区域杂草管理是目前除草作业的主流研究，而精确高效地对田间杂草进行识别和检测是实现自动化杂草管理的重要前提。但高效的识别模型往往意味着大量的农业数据。为了降低对农业标签数据的依赖性，该研究提出了一种UANP-MT (uncertainty aware and network perturbed mean teacher)的半监督语义分割网络。该模型基于PSPNet结构与MT (mean teacher)的思想，首先通过对教师网络做扩增输出，令该部分做出若干次推理并取其均值，以此来保证网络预测的鲁棒性，其次在网络的一致性学习部分构建不确定性系数来约束不同网络间的输出差异，提高预测的置信度和可靠性，从而提高模型的识别准确度。为了验证所提出的模型的有效性，设计消融试验，包括对网络参数的取值设置，特征提取网络backbone的选取，以及在不同数据量的数据集下对模型进行性能测试，试验过程中确定了模型的一些最佳的参数设置。结果表明...

UANP-MT based semi-supervised image segmentation method for identifying weeds in cabbage field

Weeds pose a great threat to modern agricultural production. The unrestrained growth of weeds has also a significant impact on crop yields and quality. Therefore, it is a high demand to effectively manage and control weeds in recent years, in order to optimize agricultural production activities. The specific area of weed management can be expected to serve as the promising research direction for weed control operations, with the rapid development of unmanned aerial vehicle (UAV) platforms and artificial intelligence (AI). Accurate and efficient identification of weeds can be one of the most key steps to realizing automated weed management in the field. However, the current efficient models of weed identification are often required a significant amount of labeled agricultural data. The major bottleneck can be also limited in the model. In this study, a semi-supervised semantic segmentation network was proposed, called UANP-MT (Uncertainty Aware and Network Perturbed Mean Teacher), in order to reduce the dependence on labeled data. The PSPNet network model was also selected to leverage the teacher-student network concept from MT (Mean Teacher). Several key improvements were then incorporated during this time. Firstly, the improved model reduced the errors that caused by random network factors. The teacher network output was also augmented to take the mean, thereby ensuring the robustness of the network prediction. Secondly, the improved model constructed the uncertainty constraints for the different uncertainties between networks and assigns weightings, in order to balance the differences in the output between different networks. This approach enhanced the confidence and reliability of the prediction, ultimately leading to the higher recognition accuracy of an improved model. A series of ablation experiments were conducted to evaluate the effectiveness of the improved model. These experiments included the setting values of network parameters, the selection of feature extraction network backbones, and the model performance testing on datasets with varying volumes of data. Some optimal parameter settings were identified for the improved model, with the ResNet50 as the preferred choice for the model backbone. The comparison experiments showed that the UANP-MT model outperformed the original supervised network, in terms of the three evaluation indicators of F1 score, pixel accuracy (PA), and intersection over union (IoU), when the labeled data was less than that of the original supervised network, indicating the superiority of the semi-supervised network. Furthermore, the effectiveness of the improved model was further validated to compare with the classic semantic segmentation networks, including SegNet, U-Net, and Deeplabv3+ on the CaiXin weed dataset. UANP-MT model achieved better performance with an f_score of 81.83%, a PA of 95.84%, and an IoU of 90.70% when only 1/4 of the labeled data was used for training. These evaluation metrics were superior to those of the Deeplabv3+ model by percentage point of 4.71, 7.94, and 8.27, respectively. The UANP-MT model was achieved in the high-quality weed detection and recognition with the low labeled data, thus significantly reducing the dependence on labeled data with the manpower and time cost saving. The findings can provide valuable implications for the subsequent weed recognition research and the development of automated weeding operations on UAV platforms.