中图分类号：TP391.41 文献标志码：A DOI: 10.16157/j.issn.0258-7998.245324 中文引用格式： 张超，刘宾，李坤. 基于改进YOLOv8的轻量化杂草识别算法研究[J]. 电子技术应用，2025，51(1)：80-85. 英文引用格式： Zhang Chao，Liu Bin，Li Kun. Research on lightweight weed recognition algorithm based on improved YOLOv8[J]. Application of Electronic Technique，2025，51(1)：80-85.
Research on lightweight weed recognition algorithm based on improved YOLOv8
Zhang Chao，Liu Bin，Li Kun
College of Information and Communication Engineering， North University of China
Abstract： Aiming at the problems of low accuracy of current field weed identification models and the difficulty of deploying multiple parameters in mobile devices and embedded devices with limited computing resources, a lightweight field weed identification model based on YOLOv8 is proposed in this paper. The model uses improved PP-LCNet to replace the original backbone network, and reduces the calculation amount of the model on the premise of ensuring the accuracy. Then, Effcient-RepGFPN is introduced as the neck network, and RFAConv is used to replace the two CSPStage modules before up-sampling. Different scale features are used to improve the performance of target detection. Finally, the MPDIoU loss function is replaced to enhance the convergence and stability of the model. Experimental results show that compared with the original model, the accuracy rate of the improved model increases by 2.1%, the recall rate increases by 2.8%, and the mAP value increases by 0.2%. Meanwhile, the size and computation amount of the model are reduced to 68.2% and 62.6% of the original model, respectively, reflecting the effectiveness of the improved algorithm in this paper.
Key words : weed identification；PP-LCNet；Effcient-RepGFPN；RFAConv；MPDIoU
