关键词: 无核白葡萄, 采摘机器人, 果梗识别, 坐标注意力机制, 可变形卷积, 视觉模型

Abstract: Accurate identification of seedless white grape stem under small trellises is the key to the automatic picking task of grape picking robot. Aiming at the problem of poor recognition effect of fresh seedless white grape stem under Small trellises in Turfan City, Xinjiang, a seedless white grape stems recognition model Small—YOLO based on YOLOv8 was proposed to realize automatic recognition of seedless white grape stem, improve the target detection head on the original model structure, and improve the feature fusion of shallow and deep networks. The ability of extracting information from the seedless white grape stem was increased. Deformable convolutional DCN is used in the shallow network to enhance the modeling ability of convolution operation on morphological changes, so that the convolutional kernel can better adapt to the irregular deformation in the input feature map, which is helpful to improve the performance of the visual model when dealing with targets of different scales, shapes and transformations, increase the coordinate attention mechanism CA, optimize the accuracy rate of the seedless white grape peduncle recognition. The test results showed that the mAP value of the improved recognition model for the seedless white grape stem reached 76.2%. Compared with YOLOv3—tiny, YOLOv5n, YOLOv6, YOLOv7, YOLOv8n and other algorithms, the mAP value was improved by 23.9%, 8%, 7.6%, 9.2%, 7%, respectively, while maintaining a fast detection speed, realizing the possibility of the seedless white grape mechanical picking under small tallows.

Key words: seedless white grapes, harvesting robot, stem recognition, coordinate attention mechanism, deformable convolution, visual model