When the images are captured by drones, the effect of oblique angles, distance variations and open environment are the main challenges for successful palm tree detection. This paper presents a method towards palm tree counting in Drone images using a novel idea of detecting dominant points by exploring Generalized Gradient Vector Flow, which defines symmetry based on gradient direction of the pixels. For each dominant point, we use angle information for classifying diagonal dominant points. It is intuition that the direction of the branches of tree converges at center of tree irrespective of the type of tree and plants. This observation motivated us to expand the direction of diagonal dominant points until it finds intersection point with another diagonal dominant point and this results in candidate points. For each candidate point, the proposed method constructs the ring by considering the distance between the intersection point and nearest neighbor candidate point as radius. This outputs region of interest and it includes center of each tree in the image. To ease the effect of complex background, we explore YOLOv5 architecture to remove false region of interests. This step results in counting oil palm trees in the mages irrespective of tree type of palm family. Experimental results on our dataset of the images captured by drones and standard dataset of coconut images captured by unmanned aerial vehicle of different trees show that the proposed method is effective and performs better than SOTA methods.

当无人机拍摄图像时，斜角、距离变化和开放环境的影响是成功检测棕榈树的主要挑战。本文提出了一种在无人机图像中计算棕榈树的方法，该方法使用一种通过探索广义梯度向量流来检测优势点的新思想，该方法定义了基于像素梯度方向的对称性。对于每个优势点，我们使用角度信息对对角线优势点进行分类。直觉上，无论树木和植物的类型如何，树枝的方向都会在树的中心会聚。这一观察促使我们扩大对角主导点的方向，直到找到与另一个对角主导点的交点，这导致候选点。对于每个候选点，所提出的方法通过将交点与最近邻候选点之间的距离视为半径来构造环。这会输出感兴趣的区域，它包括图像中每棵树的中心。为了减轻复杂背景的影响，我们探索了 YOLOv5 架构来去除虚假的兴趣区域。这一步导致计算法师中的油棕树，而不管棕榈科的树类型。在我们的无人机捕获的图像数据集和不同树的无人机捕获的椰子图像标准数据集上的实验结果表明，该方法是有效的，并且性能优于 SOTA 方法。这会输出感兴趣的区域，它包括图像中每棵树的中心。为了减轻复杂背景的影响，我们探索了 YOLOv5 架构来去除虚假的兴趣区域。这一步导致计算法师中的油棕树，而不管棕榈科的树类型。在我们的无人机捕获的图像数据集和不同树的无人机捕获的椰子图像标准数据集上的实验结果表明，该方法是有效的，并且性能优于 SOTA 方法。这会输出感兴趣的区域，它包括图像中每棵树的中心。为了减轻复杂背景的影响，我们探索了 YOLOv5 架构来去除虚假的兴趣区域。这一步导致计算法师中的油棕树，而不管棕榈科的树类型。在我们的无人机捕获的图像数据集和不同树的无人机捕获的椰子图像标准数据集上的实验结果表明，该方法是有效的，并且性能优于 SOTA 方法。