Target detection is a basic task to divide the object types in the orchard point cloud global map, which is used to count the overall situation of the orchard. And provide necessary information for unmanned navigation planning of agricultural vehicles. In order to divide the fruit trees and the ground in the point cloud global map of the standardized orchard, and provide the orchard overall information for the path planning of autonomous vehicles in the natural orchard environment. A fruit tree detection method based on the Yolo-V7 network is proposed, which can effectively detect fruit tree targets from multi-sensor fused radar point cloud, reduce the 3D point cloud information of the point cloud map to 2D for the fruit tree point cloud in the Yolo-V7 network detection map, and project the prediction results into the point cloud map. Generally, the target detection network based on PointNet has the problem of low speed and large computational load. The method proposed in this paper is fast and low computational load and is suitable for deployment in mobile robots. From the experimental results, the recall rate and accuracy rate of the proposed method in orchard fruit tree detection are 0.4 and 0.696 respectively, and its weight and reasoning time are 7.4 M and 28 ms respectively. The experimental results show that this method can achieve the robustness and efficiency of real-time detection of orchard fruit trees.

翻译：目标检测是划分果园点云全局地图中物体类型的基础任务，用于统计果园整体态势，并为农业车辆无人导航规划提供必要信息。为在标准化果园点云全局地图中区分果树与地面，并在自然果园环境中为自主车辆路径规划提供果园全局信息，提出一种基于Yolo-V7网络的果树检测方法。该方法能有效从多传感器融合雷达点云中检测果树目标，将点云地图的三维点云信息降维至二维，应用于Yolo-V7网络检测图中的果树点云，并将预测结果投影至点云地图中。通常基于PointNet的目标检测网络存在速度慢、计算量大的问题，而本文提出的方法速度快、计算量小，适用于移动机器人部署。实验结果表明，该方法在果园果树检测中的召回率和准确率分别为0.4和0.696，权重值与推理时间分别为7.4 M和28 ms。实验证明该方法能够实现果园果树实时检测的鲁棒性与高效性。