关键词: 茶嫩芽, 深度学习, 语义分割, 空洞卷积, 感受野, 采摘点定位

Abstract: In order to realize the rapid recognition of tea buds and the location of picking points, a lightweight deep learning network is studied to realize the segmentation of tea buds and the location of picking points. The combination of MobileNetV2 backbone network and dilated convolution can better balance the contradiction between the speed and accuracy of tea bud image segmentation, and meet the requirements of fast recognition of tea buds while achieving high segmentation accuracy. A picking point location method combining outer contour scanning and area threshold filtering is designed. The experiments show that the tea bud segmentation algorithm proposed in this paper has excellent accuracy in single bud tip and one bud one leafdataset, and mIoU reaches 91.65% and 91.36% respectively. While maintaining high accuracy, the model complexity of this algorithm is the lowest, with only 5.81 M parameters and 39.78 GFLOPs calculations. In the single bud tip, one bud and oneleaf, and onebud and twoleave data sets,  200 pictures were randomly selected to verify  the location of picking point, and the positioning accuracy reached 90.38%, 95.26% and 96.60% respectively.

Key words: tea bud, deep learning, semantic segmentation, dilated convolution, receptive field, picking point positioning