关键词: 茶园植保机械, 茶草检测, YOLOv3网络模型, GIoU损失

Abstract: Precise and efficient teaweed identification is the key to weed control of intelligent tea plantation protection machinery. In response to the current problems of low level of weeding intelligence in tea gardens, a teaweed detection algorithm based on the improved YOLOv3 network model is proposed. Firstly, during different seasons and periods, teaweed images are collected with a selfadaptive distance and angle in the plantations of multiple tea varieties and to build experimental data sets. Secondly, the prior anchor box scales are redesigned by the Kmeans clustering algorithm. Then, based on the YOLOv3 network model, an image area is divided by the grids of 17×17, the residual network (ResNet) is utilized as the backbone network, and the process extraction layer is added to it to improve the detection performance for weeds. Finally, the generalized intersection over union loss is introduced in the original loss function. The effectiveness of the improved algorithm is verified for teaweed detection via ablation study and comparison experiment of different target detection algorithms. The experimental results show that the detection precision and recall rate of the improved YOLOv3 network model for weeds are 85.34% and 91.38%, respectively, the highest detection precision and recall rate of tea bushes are 82.56% and 90.12%, respectively; compared with the original YOLOv3 network model, the precision is improved by 8.05%, and the frames per second transmission reaches 52.83 Hz, 16 times of the Faster R-CNN network model. These datas demonstrate that proposed algorithm in the complex environment of tea plantations not only provides better detection effect for tea bushes and green weeds, but also satisfies the requirement of realtime detection, which can provide technical support for intelligent tea plantation machinery.

Key words: tea plantation protection machinery, teaweed detection, YOLOv3 network model, GIoU loss