基于MLKA与时序RANSAC融合的梨园导航线提取方法

Navigation Line Extractionin in Pear Orchard Based on MLKA and Temporal RANSAC Fusion

摘要:

在复杂梨园环境中，传统视觉导航方法容易受到光照变化、杂草遮挡等因素的干扰。针对此问题，本文提出了一种基于改进YOLO v8模型的梨园导航线提取方法。该方法在YOLO v8模型中集成了多尺度大核注意力(Multi-scale large kernel attention，MLKA)模块以增强对树干特征的感知。设计了多帧特征点融合机制，通过记录并利用连续5帧图像中检测到的特征点，有效弥补了单帧图像特征点不足的问题。此外，引入随机抽样一致性(Random sample consensus，RANSAC)算法，分别对左右两侧树行的特征点进行降噪处理，并使用最小二乘法进行树行线拟合。通过计算左右两侧树行线的角平分线生成果园导航线。实验结果表明：改进模型在复杂的果园环境中，树干检测的精确率(Precision)达到89.8%，召回率(Recall)达到79.9%，平均精度均值(mean average precision，mAP50-95)达到了55.1%。结合多帧特征点融合与RANSAC降噪生成的导航线与手动标注的参考导航线之间的角度偏差均值为1.17°，位置偏差均值为20.40像素，均方根偏差均值为0.27。本文方法为梨园环境中的视觉导航提供了一种低成本、高适应性的技术方案。

Abstract:

Traditional visual navigation methods struggle to handle interference from varying lighting conditions and weed occlusion in complex pear orchard environments. An improved navigation line extraction method was proposed based on YOLO v8 to address this challenge. The process integrated a multi-scale large kernel attention (MLKA) module into the YOLO v8 model to enhance the perception of trunk features. A multi-frame feature point fusion mechanism was designed. By recording and utilizing feature points detected in five consecutive frames, this mechanism effectively compensated for the problem of insufficient feature points in single-frame detection. Additionally, the random sample consensus (RANSAC) algorithm was introduced to denoise the feature points of the left and right tree rows, respectively, and the least squares method was used for the two side tree rows’ line fitting. The navigation line was ultimately generated by calculating the angle bisector of the fitted lines from both sides of the tree rows. Experimental results showed that the improved model achieved a precision of 89.8%, a recall of 79.9%, and a mean average precision (mAP50-95) of 55.1% in tree trunk detection tasks under various lighting conditions and weed occlusion scenarios. The navigation line generated by combining multi-frame feature point fusion with RANSAC denoising exhibited an average angular deviation of only 1.17° from the manually annotated reference navigation line, an average position deviation of 20.40 pixels, and an average root mean square deviation of 0.27. The research result can provide a low-cost and highly adaptable technical solution for visual navigation in pear orchard environments.