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    殷献博, 邓小玲, 兰玉彬, 等. 基于改进YOLOX-Nano算法的柑橘梢期长势智能识别[J]. 华南农业大学学报, 2023, 44(1): 142-150. DOI: 10.7671/j.issn.1001-411X.202112039
    引用本文: 殷献博, 邓小玲, 兰玉彬, 等. 基于改进YOLOX-Nano算法的柑橘梢期长势智能识别[J]. 华南农业大学学报, 2023, 44(1): 142-150. DOI: 10.7671/j.issn.1001-411X.202112039
    shu
    YIN Xianbo, DENG Xiaoling, LAN Yubin, et al. Intelligent recognition of citrus shoot growth based on improved YOLOX-Nano algorithm[J]. Journal of South China Agricultural University, 2023, 44(1): 142-150. DOI: 10.7671/j.issn.1001-411X.202112039
    Citation: YIN Xianbo, DENG Xiaoling, LAN Yubin, et al. Intelligent recognition of citrus shoot growth based on improved YOLOX-Nano algorithm[J]. Journal of South China Agricultural University, 2023, 44(1): 142-150. DOI: 10.7671/j.issn.1001-411X.202112039
    shu

    基于改进YOLOX-Nano算法的柑橘梢期长势智能识别

    Intelligent recognition of citrus shoot growth based on improved YOLOX-Nano algorithm

      摘要
    • 摘要:
      目的  采用机器视觉技术开展柑橘梢期的智能感知技术研究，以解决背景与目标颜色相似造成识别精度低的问题，实现柑橘梢期自动监测，探索算法的改进方法。
      方法  根据不同卷积层提取特征的特点与不同注意力机制的作用，提出了一种基于多注意力机制改进的YOLOX-Nano智能识别模型，建立多元化果园数据集并进行预训练。
      结果  改进的YOLOX-Nano算法使用果园数据集作为预训练数据集后，各类别平均精度的平均值(Mean average precision， mAP)达到88.07%。与YOLOV4-Lite系列模型相比，本文提出的改进模型在使用较少的参数和计算量的情况下，识别精度有显著的提升，mAP分别比YOLOV4-MobileNetV3和YOLOV4-GhostNet 提升6.58%和6.03%。
      结论  改进后的模型在果园监测终端的轻量化部署方面更具有优势，为农情实时感知和智能监测提供了可行的数据和技术解决方案。

       

      Abstract:
      Objective  In order to solve the problem of low recognition accuracy due to similar color of the background and new shoots, to realize the automatic monitoring of citrus shoot stage and explore the improved method of algorithm, the machine vision technology was used to carry out the research on intelligent perceiving the growth stage of citrus shoot.
      Method  According to the characteristics of features extracted from different convolutional layers and the role of different attention mechanism, an improved YOLOX-Nano intelligent recognition model based on multi-attention mechanism was proposed, and a diversified orchard dataset for pre-training was established.
      Result  The improved YOLOX-Nano algorithm achieved the mAP (Mean average precision) of 88.07% using the orchard dataset as a pre-training dataset. Compared with the model of YOLOV4-Lite series, the improved model significantly improved the recognition accuracy with less parameters and calculation. Compared with YOLOV4-MobileNetV3 and YOLOV4-GhostNet, the mAP of improved model increased by 6.58% and 6.03% respectively.
      Conclusion  The improved model has greater advantage for lightweight deployment at orchard monitoring terminal. The findings provide feasible data and technical solutions for agricultural real-time perception and intelligent monitoring.

       

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