| 刘行谋,田 浩,杨永明,王 燕,赵小翔.复杂环境背景下绝缘子缺陷图像检测方法研究[J].电子测量与仪器学报,2022,36(2):57-67 |
| 复杂环境背景下绝缘子缺陷图像检测方法研究 |
| Research on image detection method of insulator defects in complex background |
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| DOI： |
| 中文关键词: 绝缘子 平衡交叉熵 损失函数 缺陷检测 |
| 英文关键词:insulators balanced cross entropy loss function defect detection |
| 基金项目:国家自然科学基金（51807018）、重庆市自然科学基金(cstc2020jcyj msxmX0368)项目资助 |
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| 摘要点击次数: 679 |
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| 中文摘要: |
| 针对处于复杂的环境背景下的电力绝缘子以及绝缘子缺陷的检测存在检测精度低、检测速度不高的实际问题,提出了
一种改进 YOLOv4(you only look once v4)算法的电力绝缘子图像以及存在缺陷的绝缘子检测的方法。 通过制作电力绝缘子以
及绝缘子存在缺陷的数据集,使用 K-均值聚类(K-means)算法对电力绝缘子图像样本进行聚类,获得不同大小的先验框参数;
然后通过改进平衡交叉熵(balanced cross entropy, BCE)引入一个权重系数,来增加损失函数的贡献程度;最后,通过增加空间
金字塔池化结构(spatial pyramid pooling, SPP)前后的卷积层来加深网络的深度。 实验结果表明,改进模型的单张检测时间为
3. 27 s, 对于绝缘子缺陷平均检测精度比原始的 YOLOv4 算法提升了 24. 36%。 同时通过改进后的 YOLOv4 算法在测试集上的
平均精度均值(mean average precision, mAP)的值为 84. 05%,比原始的 YOLOv4 算法提升了 17. 83%,充分说明了能够很好的定
位和识别电力绝缘子图像存在的缺陷。 |
| 英文摘要: |
| Aiming at the actual problems of low detection accuracy and low detection speed in the detection of power insulators and
insulator defects in a complex environment background, an improved you only Look once v4(YOLOv4) algorithm for power insulator
images and existence Method of detecting defective insulators is proposed. By making a dataset of power insulators and insulators with
defects, using K-means clustering (K-means) algorithm to cluster the power insulator image samples to obtain different sizes of a priori
box parameters; then by improving the balance cross entropy (Balanced Cross Entropy, BCE), it introduces a weight coefficient to
increase the contribution of the loss function. Finally, the depth of the network is deepened by adding convolutional layers before and
after the spatial pyramid pooling ( SPP) structure. The experimental results show that the single sheet detection time of the improved
model is 3. 27 s, and the average detection accuracy of insulator defects is improved by 24. 36% compared with the original YOLOv4
algorithm. At the same time, through the improved YOLOv4 algorithm, the value of mean average precision(mAP) on the test set is
84. 05%, which is 17. 83% higher than the original YOLOv4 algorithm, which fully demonstrates the ability to locate and identify of the
defect in power insulator images well. |
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