An Object Detection Algorithm for Orchard Vehicles Based on AGO-PointPillars

With the continuous expansion of the orchard planting area, there is an urgent need for autonomous orchard vehicles that can reduce the labor intensity of fruit farmers and improve the efficiency of operations to assist operators in the process of orchard operations. An object detection system that can accurately identify potholes, trees, and other orchard objects is essential to achieve unmanned operation of the orchard vehicle. Aiming to improve upon existing object detection algorithms, which have the problem of low object recognition accuracy in orchard operation scenes, we propose an orchard vehicle object detection algorithm based on Attention-Guided Orchard PointPillars (AGO-PointPillars). Firstly, we use an RGB-D camera as the sensing hardware to collect the orchard road information and convert the depth image data obtained by the RGB-D camera into 3D point cloud data. Then, Efficient Channel Attention (ECA) and Efficient Up-Convolution Block (EUCB) are introduced based on the PointPillars, which can enhance the ability of feature extraction for orchard objects. Finally, we establish an orchard object detection dataset and validate the proposed algorithm. The results show that, compared to the PointPillars, the AGO-PointPillars proposed in this study has an average detection accuracy improvement of 4.64% for typical orchard objects such as potholes and trees, which can prove the reliability of our algorithm.