基于先验嵌入与多尺度特征融合的耕后水稻单根秸秆语义分割方法

Semantic Segmentation of Individual Straw Stalks after Plowing Based on Prior Embedding and Multi-scale Feature Fusion

摘要:

耕后土壤表面秸秆覆盖率是评估粘秆还田质量的重要参数，现有方法难以有效识别水稻单根秸秆，为此提出 一种融合先验嵌入与多尺度特征融合的语义分割方法，以实现水稻单根粘秆的准确识别。采用基于颜色距离的固 定阈值分割法对原始图像进行预处理并生成先验图，在抑制背景干扰情况下为后续识别任务提供先验信息输人;设计了一种改进 U-Net 的 MRCF-DA-CDPE 模型，采用并行多尺度卷积捕获从细碎粘秆到大型粘秆聚集区等不 同尺度的信息特征，利用通道与空间注意力筛选关键特征并抑制土壤亮斑等干扰，将预处理中的连续距离信息作 为额外输人通道嵌人网络，为模型分割提供物理引导。图像预处理策略表明，该方法使各基础模型的平均精度提升了 2~4 个百分点，其中 U-Net 的识别精度最高。改进模型验证试验表明，MRCF-DA-CDPE 模型的平均交并比、平均精度和 Kappa 系数分别达到了 86.93%、94.89% 和 0.850 2, 相比基础 U-Net 模型分别提升了 2.72、2.98 个百分点和 0.0567。本方法实现了对单根粘秆的精细识别，可为秸秆还田质量检测、耕整效果评估等提供技术支撑。

Abstract:

Straw coverage on the soill surface after tillage is a key parameter for evaluating the quality of straw return to the field. Existing methods struggle to effectively identify individual rice straw stalks. To address this, a semantic segmentation method that integrated prior embedding with multi-scale feature fusion was proposed to achieve accurate recognition of individual rice straw stalks. A fixed-threshold segmentation method based on color distance was employed to preprocess the original image and generate a prior map, providing prior information input for subsequent recognition tasks while suppressing background interference. An enhanced U - Net model, MRCF - DA - CDPE, was designed. It employed parallel multi-scale convolutions to capture information features across scales--from fragmented straw fragments to large straw clusters-while utilizing channel and spatial attention to select key features and suppress disturbances like soil bright spots. Continuous distance information from preprocessing was embedded into the network as an additional input channel, providing physical guidance for segmentation. Image preprocessing strategies demonstrated that this approach enhanced the average accuracy of each base model by 2 ~ 4 percentage points, with U - Net achieving the highest recognition accuracy. Validation tests of the improved model demonstrated that the MRCF - DA - CDPE model achieved 86.93% average intersection-over-union ratio (I0U), 94.89% average precision, and 0. 850 2 Kappa coefficient, representing improvements of 2. 72, 2.98 percentage points and 0. 056 7 respectively over the baseline U - Net model. This method achieved precise recognition of individual straw stalks, providing technical support for straw return quality inspection and tillage effectiveness evaluation.