关键词: 蔗芽识别, YOLOv4-Tiny, 树莓派4B, SE-Resnet, Kmeans重聚类

Abstract: In order to solve the problem that the speed of sugarcane bud recognition does not match in the directional mechanized planting of sugarcane seeds, this paper proposes a rapid recognition method of sugarcane buds based on the improved YOLOv4-Tiny. The backbone network is integrated with the SE-Resnet module to realize the attention mechanism to enhance the characteristics of sugarcane buds. The neck network structure adds an additional layer of prediction scale and performs Kmeans reclustering of the anchor box, and uses the detailed information of the shallow network to improve the performance. The detection ability of the model to small target sugarcane buds, the design of humancomputer interaction interface displays the sugarcane bud identification and positioning information in real time. In this study, the improved YOLOv4-Tiny algorithm and NCS2 accelerated reasoning will be deployed in the Raspberry Pi 4B device for testing. The test results show that the recognition accuracy of sugarcane buds is 95.87%, and the average precision and mean mAP is 92.46%. Based on the Raspberry Pi, the detection speed is 0.61 s, and the model size is only 23.2 MB. It realizes accurate and fast identification of sugarcane buds deployed in embedded devices, solves the problem that the slow recognition speed of sugarcane seeds restricts the speed of mechanized sowing of sugarcane seeds, and provides a solution for mechanized directional planting of sugarcane seeds.

Key words: sugarcane sprout identification, YOLOv4-Tiny, Raspberry Pi 4B, SE-Resnet, Kmeans reclustering