Improved keypoint localization network for tea bud based on YOLO framework

Abstract

The precise localization of picking points is the primary challenge that should be addressed by the robotic tea bud harvester. The efficient identification and accurate positioning of picking points directly affects the efficiency of tea buds harvesting. Compared with the target detection and semantic segmentation algorithms, the keypoint detection algorithms do not require to incorporate target shape features for accurately localizing the picking point, which allows to significantly decrease the computational load, and thus facilitates the subsequent point cloud processing. This study introduces a tea bud keypoint detection network referred to as TBKNet. This model uses the concept of structural reparameterization to reconstruct the CSPDarknet53 backbone network, and constructs an asymptotic parallel feature fusion network by incorporating the CA attention mechanism into the asymptotic feature pyramid network. In addition, in order to accelerate the convergence of the model, a keypoint regression loss function, which considers the angle and Euclidean distance, is proposed. The experimental results show that TBNKet reaches the highest accuracy with an average precision of 87.1 % for keypoint detection. It has 5.7 %, 16.9 %, and 3.3 % higher mAP values compared with the DEKR, YOLOPose, and YOLOPv8-Pose models, respectively. This is efficient in identifying and localizing keypoints of tea buds, and it can provide guidance for similar intensive harvesting tasks.