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基于改进YOLOv5s的烟火轻型检测算法
作者:
作者单位:

1.武汉科技大学冶金装备及其控制教育部重点实验室 武汉 430081; 2.武汉科技大学机械传动与控制工程湖北省重点实验室 武汉 430081

中图分类号:

TP391；TN911.73

基金项目:

国家自然科学基金（51975431）项目资助


Light detection algorithm of pyrotechnics based on improved YOLOv5s
Author:
  • Zhao Songhuai

    Zhao Songhuai

    1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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  • Zhou Min

    Zhou Min

    1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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  • Shen Fei

    Shen Fei

    1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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  • Xiang Feng

    Xiang Feng

    1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China； 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China
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Affiliation:

1.Key Laboratory of Metallurgical Equipment and Its Control, Ministry of Education, Wuhan University of Science and Technology， Wuhan 430081, China; 2.Hubei Key Laboratory of Mechanical Transmission and Control Engineering, Wuhan University of Science and Technology，Wuhan 430081, China

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    摘要:

    针对传统传感器对烟火检测不及时且无法给出烟火详细信息，当前主流烟火检测算法检测效率与精度不平衡等问题，提出了一种改进YOLOv5s的烟火轻型检测算法。将Backbone中第2个卷积模块替换成Stem模块，在提高模型对小目标空间信息检测性能的同时有效地控制了总体的浮点运算数；在Backbone和Neck中引入C3Ghost模块和Ghost卷积模块，以达到减少网络参数数量和提高烟火检测性能的目的；为了区分特征融合过程中不同特征的重要性，提出了一种在PAN中添加可学习权重参数的结构，显著提高了对烟火检测的平均精度。实验结果表明：与原模型相比，模型的权重从14.4 M减小到10.2 M，GFLOPs从15.8减小到3.7，平均精度提升了1.1%。改进的模型在轻量化的同时提升了对烟火检测的性能。

    Abstract:

    Aiming at the problems of the traditional sensor′s late detection of fireworks and its inability to give details of fireworks, as well as the imbalance between detection efficiency and accuracy of the current mainstream fireworks detection algorithms, an improved YOLOv5s light detection algorithm for fireworks was proposed. The second convolutional module in Backbone is replaced with Stem module, which can improve the model′s detection performance of small target space information and effectively control the total floating point operand. C3Ghost module and Ghost convolution module are introduced in Backbone and Neck to reduce the number of network parameters and improve the performance of fireworks detection. In order to distinguish the importance of different features in the process of feature fusion, a structure of adding learnable weight parameters to PAN is proposed, which significantly improves the average accuracy of fireworks detection. The experimental results show that compared with the original model, the weight of the model is reduced from 14.4 M to 10.2 M, GFLOPs is reduced from 15.8 to 3.7, and the average accuracy is increased by 1.1%. The improved model has improved the performance of pyrotechnic detection while being lightweight.

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引用本文

赵松怀,周敏,申飞,向峰.基于改进YOLOv5s的烟火轻型检测算法[J].电子测量技术,2024,47(17):140-146

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  • 在线发布日期: 2024-12-11
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