Three-Dimensional Spatial Perception of Blueberry Fruits Based on Improved YOLOv11 Network

The automated harvesting of blueberries using a picking robot places a greater demand on the 3D spatial perception performance, as the robot’s grasping mechanism needs to pick blueberry fruits accurately at specific positions and in particular poses. To achieve this goal, this paper presents a method for blueberry detection, 3D spatial localization, and pose estimation using visual perception, which can be deployed on an OAK depth camera. Firstly, a blueberry and calyx scar detection dataset is constructed to train the detection network and evaluate its performance. Secondly, the blueberry and calyx scar detection model based on a lightweight YOLOv11 (the eleventh version of You Only Look Once) network with an improved depth-wise separable convolution (DSC) module is designed, and a 3D coordinate system relative to the camera is established to calculate the 3D pose of the blueberry fruits. Finally, the above detection model is deployed using the OAK depth camera, leveraging its depth estimation module and three-axis gyroscope to obtain the 3D coordinates of the blueberry fruits. The experimental results demonstrate that the method proposed in this paper can accurately identify blueberry fruits at various maturity levels, achieving a detection accuracy of 95.8% mAP50-95, a maximum positioning error of 7.2 mm within 0.5 m, and an average 3D pose error of 19.2 degrees (around 10 degrees at the ideal picking angle) while maintaining a detection frame rate of 13.4 FPS (frames per second) on the OAK depth camera, providing effective picking guidance for the mechanical arm of picking robots.