In this letter, we propose a Double Strand robotic grasp detection Network (DSNet), that combines a transformer branch and a U-Net branch within an encoder-decoder structure. The DSNet is designed to reconcile differences between these two approaches and provide access to both local and global resources. We have strategically incorporated bidirectional bridges with cross-attention mechanisms at the bottleneck points of each branch. These bridges facilitate the retrieval of abstract semantic data and reciprocally transfer it to the alternate branch, preserving both local features and global representations. To validate the performance of the DSNet, we utilized complete RGB-D information as input. The DSNet achieves an impressive accuracy rate of 98.31% and 95.7% on the Cornell and Jacquard grasping datasets. We used a 6DoF AUBO i5 robot to perform full-angle grasping of unknown objects, thereby confirming the reliability of the model. The source code for DSNet can be accessed at https://github.com/VoryKwin/DSNet.

An accurate and efficient detection method for harvesting is crucial for the development of automated harvesting robots in short-cycle, high-yield facility tomato cultivation environments. This study focuses on cherry tomatoes, which grow in clusters, and addresses the complexity and reduced detection speed associated with the current multi-step processes that combine target detection with segmentation and traditional image processing for clustered fruits. We propose YOLO-Picking Point (YOLO-PP), an improved cherry tomato picking point detection network designed to efficiently and accurately identify stem keypoints on embedded devices. YOLO-PP employs a C2FET module with an EfficientViT branch, utilizing parallel dual-path feature extraction to enhance detection performance in dense scenes. Additionally, we designed and implemented a Spatial Pyramid Squeeze Pooling (SPSP) module to extract fine features and capture multi-scale spatial information. Furthermore, a new loss function based on Inner-CIoU was developed specifically for keypoint tasks to further improve detection efficiency.The model was tested on a real greenhouse cherry tomato dataset, achieving an accuracy of 95.81%, a recall rate of 98.86%, and mean Average Precision ($mAP$) scores of 99.18% and 98.87% for mAP50 and mAP50-95, respectively. Compared to the DEKR, YOLO-Pose, and YOLOv8-Pose models, the mAP value of the YOLO-PP model improved by 16.94%, 10.83%, and 0.81%, respectively. The proposed algorithm has been implemented on NVIDIA Jetson edge computing devices, equipped with a human–computer interaction interface. The results demonstrate that the proposed Improved Picking Point Detection Network exhibits excellent performance and achieves real-time accurate detection of cherry tomato harvesting tasks in facility agriculture.