复杂环境背景下绝缘子缺陷图像检测方法研究

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。

Research on image detection method of insulator defects in complex background

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.