Insulator detection based on SE-YOLOv5s

Qing TIAN; Rong HU; Zuoyong LI; Yuanzheng CAI; Zhaochai YU

doi:10.11959/j.issn.2096-6652.202132

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Insulator detection based on SE-YOLOv5s

Special Issue: Intelligent Object Detection and Recognition | 更新时间：2024-06-05

- Insulator detection based on SE-YOLOv5s
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 3, Pages: 312-321(2021)
- 作者机构：
  
  1. 福建工程学院计算机科学与数学学院，福建福州 350118
  2. 闽江学院计算机与控制工程学院，福建福州 350121
  3. 福建省信息处理与智能控制重点实验室，福建福州 350121
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202132
  CLC： TP391.41, TM216
- Published Online：2021-09，
  
  Published：15 September 2021
- 稿件说明：
移动端阅览
Qing TIAN, Rong HU, Zuoyong LI, et al. Insulator detection based on SE-YOLOv5s[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 312-321.
DOI：

Qing TIAN, Rong HU, Zuoyong LI, et al. Insulator detection based on SE-YOLOv5s[J]. Chinese journal of intelligent science and technology, 2021, 3(3): 312-321. DOI： 10.11959/j.issn.2096-6652.202132.

摘要

在电力系统需要巡检的大环境下，人工巡检的传统方式存在很大不便和安全隐患，而采用无人机的目标检测方法在绝缘子检测识别方向有很大的应用前景。针对绝缘子图像检测中存在的场景复杂、视角多变、设备计算能力受限等问题，提出了一种改进的轻量级SE-YOLOv5s卷积神经网络来实现对绝缘子的快速目标检测，该方法首先在YOLOv5s网络中融合SE注意力模块，以强化网络对绝缘子目标的辨识能力，随后采用K-means聚类方法构建绝缘子的先验框，以提升定位精度，最后构造置信度与定位任务联合的损失函数，并引入 Mosaic 数据增强策略训练网络，有效解决训练数据不足的问题。经过实验验证发现，与主流目标检测方法相比，提出的SE-YOLOv5s方法在绝缘子检测准确率、召回率、检测速度及平均精度均值等性能指标上均取得了较好的结果。实验结果表明，该网络对于绝缘子检测有很好的效果，具有更好的鲁棒性，对电力系统的巡检方式具有参考价值。

Abstract

In a large environment where the power system needs to be inspected

the traditional method of manual inspection has great inconvenience and potential safety risks

and the object detection method of unmanned aerial vehicle has great application prospects in the direction of insulator detection and recognition.SE-YOLOv5s

a lightweight insulator detection network that performs efficient detection for this task was presented.Firstly

backbone of YOLOv5s by fusing the SE attention module was strengthen.Then

the position distribution of insulator object was investigated and predefined templates of a priori box by K-means clustering on prior coordinate vectors were generated.Finally

the network by a multitask loss function was trained combined with confidence and position regression task.Furthermore

Mosaic data augmentation was utilized to supplement additional training samples.Experimental results demonstrate that the proposed SE-YOLOv5s significantly outperforms baseline methods at multiple criterions including accuracy

recall rate

detection rate and mean average precision.In comparison with the baselines

the proposed network has a flexible trade-off between robustness and memory overhead and it is a potential approach to promote the power system development.

关键词

Keywords

references

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