余建国, 丁元昊, 王雯, 靳梦欣. 基于改进型YOLOX的储粮害虫识别技术研究[J]. 河南工业大学学报自然科学版, 2024, 45(4): 117-125. DOI: 10.16433/j.1673-2383.202311070002
    引用本文: 余建国, 丁元昊, 王雯, 靳梦欣. 基于改进型YOLOX的储粮害虫识别技术研究[J]. 河南工业大学学报自然科学版, 2024, 45(4): 117-125. DOI: 10.16433/j.1673-2383.202311070002
    YU Jianguo, DING Yuanhao, WANG Wen, JIN Mengxin. Research on identification technology of stored grain pests based on improved YOLOX[J]. Journal of Henan University of Technology(Natural Science Edition), 2024, 45(4): 117-125. DOI: 10.16433/j.1673-2383.202311070002
    Citation: YU Jianguo, DING Yuanhao, WANG Wen, JIN Mengxin. Research on identification technology of stored grain pests based on improved YOLOX[J]. Journal of Henan University of Technology(Natural Science Edition), 2024, 45(4): 117-125. DOI: 10.16433/j.1673-2383.202311070002

    基于改进型YOLOX的储粮害虫识别技术研究

    Research on identification technology of stored grain pests based on improved YOLOX

    • 摘要: 为平衡储粮害虫图像检测中的速度与精度以获得二者最优结合的目标检测模型,提出并实现了一种基于改进YOLOX网络模型的储粮害虫检测方法。通过引入一种新的GSConv替换标准卷积以减少计算量,结合引入的Res-CBAM注意力机制,提升模型的特征提取能力;引入SiLU激活函数提升模型训练效率;引入双向特征金字塔网络(BiFPN)改善特征融合效果;改进损失函数提高目标框回归稳定性;使用非极大值抑制解决冗余框过剩的问题。试验结果表明:改进后的YOLOX模型计算量减少了31%;检测速度提高了18%,最高可达60 FPS;检测精度提高了6.14百分点,最高可达97.05%。改进的YOLOX模型结合了一阶段算法和二阶段算法的优点,在储粮害虫的智能识别中取得了明显的效果。

       

      Abstract: To balance the speed and precision of stored grain pest image detection and obtain the optimal target detection model, an improved YOLOX network model was proposed and implemented for stored grain pest detection. By introducing a new GSConv to replace the standard convolution, and combining with the introduced Res-CBAM attention mechanism, the feature extraction capability of the model was improved and the computational load was reduced. SiLU activation function and batch normalization (BN) was introduced to improve model training efficiency. Bidirectional feature pyramid network (BiFPN) was introduced to improve the feature fusion effect. The loss function was improved to enhance the stability of target box regression, and non-maximum suppression was used to solve the problem of redundant boxes. The experimental results showed that the computational load of the improved YOLOX model was reduced by 31%. The detection speed was increased by 18%, up to 60 FPS, and the detection accuracy was improved by 6.14 percentage paints, up to 97.05%. The improved YOLOX model combined the advantages of one-stage algorithm and two-stage algorithm, and achieved obvious results in the intelligent identification of stored grain pests.

       

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