基于改进Cascade RCNN的输电线路防振锤脱落检测方法

doi:10.11996/JG.j.2095-302X.2023050849

图学学报 ›› 2023, Vol. 44 ›› Issue (5): 849-860.DOI: 10.11996/JG.j.2095-302X.2023050849

• 图像处理与计算机视觉 • 上一篇下一篇

基于改进Cascade RCNN的输电线路防振锤脱落检测方法

阎光伟(), 刘润泽, 焦润海(), 何慧

华北电力大学控制与计算机工程学院，北京 102200

收稿日期:2022-12-30 接受日期:2023-05-15 出版日期:2023-10-31 发布日期:2023-10-31
通讯作者: 焦润海(1977-)，男，教授，博士。主要研究方向为图像识别、机器学习和数据挖掘。E-mail：runhaijiao@ncepu.edu.cn
作者简介:阎光伟(1971-)，男，副教授，博士。主要研究方向为计算机图形、图像及信息安全。E-mail：yan_guang_wei@126.com
基金资助:
国家自然科学基金项目(62073133)

Detection method of dropped anti-vibration hammer for transmission line based on improved Cascade RCNN

YAN Guang-wei(), LIU Run-ze, JIAO Run-hai(), HE Hui

School of Control and Computer Engineering, North China Electric Power University, Beijing 102200, China

Received:2022-12-30 Accepted:2023-05-15 Online:2023-10-31 Published:2023-10-31
Contact: JIAO Run-hai (1977-), professor, Ph.D. His main research interests cover image recognition, machine learning and data mining. E-mail：runhaijiao@ncepu.edu.cn
About author:YAN Guang-wei (1971-), associate professor, Ph.D. His main research interests cover computer graphics, image and information security. E-mail：yan_guang_wei@126.com
Supported by:
National Natural Science Foundation of China(62073133)

摘要/Abstract

摘要：

无人机巡检输电线路时，因拍摄角度和距离问题，容易出现被输电线遮挡和远距离拍摄的防振锤脱落目标，导致目标特征被遮挡且分辨率较低，且部分防振锤出现滑移现象，导致目标识别准确率降低。针对以上问题，提出一种基于改进Cascade RCNN的防振锤脱落检测网络。第一，设计了对比学习网络，将正负样本与真实样本的特征进行对比学习，利用对比损失函数训练网络，使其能更加关注到被遮挡的防振锤脱落目标，提升其特征提取能力；第二，进行了分类器增强操作，筛选出网络级联结构中回归效果较好的感兴趣区域并送入最后的分类回归队列中，提高了分类器的分类能力，进而提升检测目标的分类分数；第三，设计了并行注意力机制模块，整合网络提取的特征，增大关键特征的权重，使网络关注到图像中更关键的区域；在特征金字塔中，将双线性插值方法代替为反卷积，提升特征还原能力。经交叉验证实验结果表明，改进后的模型召回率、精确率和平均精度达到了97.5%，91.0%和92.0%，相比基线模型分别提高了6.9%，28.4%和8.0%。

关键词: 输电线路, 防振锤脱落, Cascade RCNN, 对比学习网络, 并行注意力模块, 分类器增强, 样本相似度

Abstract:

During the inspection of transmission lines using Unmanned Aerial Vehicle (UAV), there are many dropped anti-vibration hammers that become obstructed by wires or are shot from a distance. This challenge leads to the occlusion of target features and low resolution. In addition, the close proximity of a number of hammers due to sliding poses challenges to the accuracy of target identification. To address the above problems, a deep neural network based on an improved Cascade RCNN was proposed to identify the dropped anti-vibration hammers. The proposed network mainly achieved improvements from the following four aspects. First of all, a contrastive learning network was designed to compare the features of positive and negative samples with real samples. By utilizing a contrastive loss function during network training, the network became more attentive to the blocked dropped anti-vibration hammers and enhanced its feature extraction ability. Secondly, the classifier was enhanced. The selection of interested regions with better regression performance in the cascade structure was filtered and input directly into the final classification regression queue. This improved the classification performance of the classifier, thereby enhancing the classification scores of the detected targets. Thirdly, a parallel attention mechanism module was designed to integrate the extracted features from the network, increasing the weights of key features and directing the network’s attention to more critical features in the image. In addition, in the process of feature fusion of the feature pyramid, the bilinear interpolation method was replaced with deconvolution to enhance the feature restoration capability. The experimental results demonstrated that the improved model achieved a recall rate of 97.5%, precision of 91.0%, and average precision of 92.0%, an improvement of 6.9%, 28.4%, and 8.0%, respectively, compared with the baseline model.

Key words: transmission line, dropped anti-vibration hammer, Cascade RCNN, contrastive learning network, parallel attention module, classifier enhancement, sample similarity

中图分类号:

TP391.4

阎光伟, 刘润泽, 焦润海, 何慧. 基于改进Cascade RCNN的输电线路防振锤脱落检测方法[J]. 图学学报, 2023, 44(5): 849-860.

YAN Guang-wei, LIU Run-ze, JIAO Run-hai, HE Hui. Detection method of dropped anti-vibration hammer for transmission line based on improved Cascade RCNN[J]. Journal of Graphics, 2023, 44(5): 849-860.

图/表 25

图1 Cfce-Pa-Net网络结构

Fig. 1 Cfce-Pa-Net network architecture

图2 对比学习网络结构

Fig. 2 Contrastive learning network structure

图3 不同阈值α下的检测结果

Fig. 3 Different thresholds α test results under ((a) α=0; (b) α=0.5)

图4 ROI位置回归变化统计图

Fig. 4 ROI position regression change statistical chart

图5 分类器增强结构图

Fig. 5 Classifier enhancement structure diagram

图6 并行注意力模块设计图

Fig. 6 Design diagram of parallel attention module

表1 混淆矩阵

Table 1 Confusion matrix

真实情况	预测结果
真实情况	正例	反例
正例	TP (真正例)	FN (假负例)
反例	FP (假正例)	TN (真负例)

图7 Cfce-Pa-Net的检测结果((a)被遮挡目标检测；(b)远距离目标检测；(c)滑移目标检测)

Fig. 7 The detection results of Cfce-Pa-Net ((a) For occluded object detection; (b) Long-range target detection; (c) Sliding target detection)

表2 本文提出的模型与其他模型的比较

Table 2 Comparison between the model proposed in this paper and other models

Model	Backbone	Contrastive loss	Classifier enhancement	Parallel attention	AP (%)	Time
Model	Backbone	Contrastive loss	Classifier enhancement	Parallel attention	AP (%)	Train (h)	Test (s)
SSD	VGG-16	×	×	×	75.3	5.1	0.160
YOLOv4^[9]	Darknet-53	×	×	×	80.7	6.4	0.165
YOLOv5^[10]	CSPDarknet53+Focus	×	×	×	86.3	7.5	0.113
Faster RCNN^[7]	ResNet-101	×	×	×	83.0	13.7	0.165
Cascade RCNN^[8]	ResNet-101	×	×	×	84.0	10.0	0.160
Cfce-Pa-Net1	ResNet-101	√	×	×	89.1	15.3	0.169
Cfce-Pa-Net2	ResNet-101	√	√	×	90.3	17.3	0.169
Cfce-Pa-Net3	ResNet-101	√	√	√	92.0	20.1	0.171
Cfce-Pa-Net3	ResNet-50	√	√	√	91.8	19.3	0.168

表3 比较不同相似度计算方式对网络的影响(%)

Table 3 Comparing the impact of different similarity calculation methods on networks (%)

相似度计算方式	AP	Precision	Recall
欧式距离	89.1	78.7	95.0
余弦相似度	88.2	48.2	94.9
点积相似度	88.3	69.1	93.5

图8 特征热力图((a)原始图片；(b)改进前网络提取的特征热力图；(c)改进后网络提取的特征热力图)

Fig. 8 Characteristic thermal map ((a) Original image; (b) The feature heat map extracted by the network before improvement; (c) Improved network extracted feature thermal maps)

图9 正样本与真实样本欧式距离分布变化图

Fig. 9 Changes in Euclidean distance distribution between positive and real samples

图10 分类回归损失变化图((a)分类损失；(b)回归损失)

Fig. 10 Classification regression loss change chart ((a) Classification loss; (b) Regression loss)

表4 不同阈值β的实验结果

Table 4 Different thresholds β experimental results

方法	阈值β	AP (%)	Precision (%)	Recall (%)
不加入分支	-	89.1	78.7	95.0
加入分支	0.80	80.7	66.0	88.7
	0.85	84.1	62.9	90.1
	0.90	87.0	57.4	93.7
	0.95	90.3	89.0	95.9
	0.96	90.1	85.8	96.3
	0.97	90.1	80.8	96.2

图11 分类器增强前后的结果对比((a)改进后；(b)改进前)

Fig. 11 Comparison of results before and after classifier enhancement ((a) Improved; (b) Before improvement)

表5 消融实验效果对比表(%)

Table 5 Comparison table of ablation experiment effects (%)

方法	Recall	Precision	AP
Cascade RCNN^[8]	95.9	89.0	90.3
Cascade RCNN^[8]+通道注意力	95.7	80.8	90.4
Cascade RCNN^[8]+空间注意力	95.9	85.8	90.6
Cascade RCNN^[8]+并行注意力	97.5	91.0	92.0

图12 热力图可视化((a)原始图像；(b)改进前的热力图；(c)改进后的热力图)

Fig. 12 Visualization of thermal maps ((a) Original images; (b) Thermal diagram before improvement; (c) Improved thermal diagram)

表6 反卷积和双线性插值方法对比实验(%)

Table 6 Comparative experiment of deconvolution and bilinear interpolation methods (%)

上采样方式	Recall	Precision	AP
反卷积	97.5	91.0	92.0
双线性插值	97.0	88.6	91.3

表7 基线网络五折交叉验证实验结果(%)

Table 7 Baseline network cross validation experimental results (%)

测试集	Recall	Precision	AP
1	90.1	62.9	84.1
2	90.8	60.1	83.7
3	90.2	66.2	84.3
4	92.8	57.5	83.9
5	89.0	66.3	84.2

表8 对比学习网络五折交叉验证实验结果(%)

Table 8 Contrastive learning network cross validation experimental results (%)

测试集	Recall	Precision	AP
1	95.6	75.4	88.9
2	96.2	82.7	89.2
3	92.3	80.2	88.8
4	93.2	85.6	89.2
5	97.6	69.5	89.3

表9 分类器增强五折交叉验证实验结果(%)

Table 9 Experimental results of classifier enhancement cross validation (%)

测试集	Recall	Precision	AP
1	95.1	89.2	90.3
2	96.2	89.9	90.4
3	96.3	85.8	90.1
4	96.2	90.0	90.5
5	95.6	90.0	90.1

表10 并行注意力五折交叉验证实验结果(%)

Table 10 Experimental results of parallel attention cross validation (%)

测试集	Recall	Precision	AP
1	95.6	90.4	91.9
2	98.6	88.4	92.1
3	98.1	92.0	91.8
4	97.0	92.8	92.1
5	98.3	91.5	91.9

图13 绝缘子自爆缺陷((a)，(b)不同视角下绝缘子自爆)

Fig. 13 Insulator self explosion defect ((a), (b) Insulator self explosion defects from different perspectives)

表11 绝缘子自爆数据集实验结果(%)

Table 11 Experimental results of insulator self explosion dataset (%)

方法	AP	Precision	Recall
Cascade RCNN^[8]	85.4	50.9	88.6
Cfce-Pa-Net1	87.7	59.7	91.9
Cfce-Pa-Net2	89.2	81.2	91.3
Cfce-Pa-Net3	89.6	81.4	91.1

图14 模型改进前后检测结果对比((a)改进前；(b)改进后)

Fig. 14 Comparison of detection results before and after model improvement (a) Before improvement; (b) After improvement)

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基于改进Cascade RCNN的输电线路防振锤脱落检测方法

Detection method of dropped anti-vibration hammer for transmission line based on improved Cascade RCNN

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