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Received：22 August 2022，
Revised：2023-01-20，
Published：16 November 2023
移动端阅览
目的
2
防震锤可以减少输电线路的周期性震动以降低线路的疲劳损害，定期对防震锤进行巡检非常必要。针对目前无人机巡检输电线路所得航拍图像背景复杂，而种类较多、形状各异以及特性不一的防震锤在航拍图像中占据像素面积很小，导致防震锤检测过程中出现的检测精度低、无法确定缺陷类型等问题，提出了一种结合跨层特征融合和级联检测器的防震锤缺陷检测方法。
方法
2
本文使用无人机对防震锤部件巡检的航拍图像进行数据扩充建立防震锤缺陷检测数据集，并划分了4种缺陷类型，为研究提供了数据基础。首先，以VGG16（Visual Geometry Group 16-layer network）为基础对1、3、5层特征进行特征融合得到特征图，平衡了语义信息和空间特征；其次，使用3个级联检测器对目标进行分类定位，减小了交并比（intersection over union，IoU）阈值对网络性能的影响；最后，将非极大值抑制法替换为Soft-NMS（soft non-maximum suppression）算法，去除边界框保留了最佳结果。
结果
2
在自建数据集上验证网络模型对4种防震锤缺陷类型的检测效果，与现有基于深度学习的其他6种先进算法相比，本文算法的平均准确率比Fast R-CNN（fast region-based convolutional network）、Faster R-CNN、YOLOv4 （you only look once version 4）分别提高了13.5%、3.4%、5.8%，比SSD300 （single shot MultiBox detector 300）、YOLOv3、RetinaNet分别提高了9.5%、8.5%、8%。与Faster R-CNN相比，本文方法的误检率降低了5.61%，漏检率降低了3.01%。
结论
2
本文提出的防震锤缺陷检测方法对不同背景、不同光照、不同角度、不同尺度、不同种类和不同缺陷种类的防震锤均有较好的检测结果，不但可以更好地提取防震锤的特征，而且还能提高分类和位置预测网络的定位精度， 从而有效提高了防震锤缺陷检测算法的准确率，在满足防震锤巡检工作实际检测要求的同时还具有较好的鲁棒性和有效性。
Objective
2
Anti-vibration hammers can reduce the periodic vibration of transmission lines to minimize line fatigue damage. Therefore， a regular inspection of these hammers is necessary. Previous studies on anti-vibration hammers for transmission lines have mostly focused on the identification or fault classification of anti-vibration hammers with only one case of deficiency. However， the detection methods for such small-scale targets are limited， and their leakage and false detection rates remain very high， which cannot guarantee the safe operation of transmission lines. Therefore， in view of the complex background of the aerial images obtained from the current unmanned aerial vehicle （UAV） inspection of transmission lines， the different types， shapes， and characteristics of anti-vibration hammers occupying a small pixel area in these aerial images can lead to problems， such as inability to determine the types of defects and the low detection accuracy in the anti-vibration hammer detection process. In this paper， we propose an anti-shock hammer defect detection method based on cross-layer feature fusion and cascade detector.
Method
2
Given the lack of a public anti-vibration hammer dataset at home and abroad for targeted research， this paper takes the aerial images taken via the UAV inspection of anti-vibration hammer components as the original data and then expands the dataset by geometric and contrast transformation to ensure the equalization of the number of different types of anti-vibration hammers in the dataset and to establish an anti-vibration hammer defect detection dataset. The anti-vibration hammer defects are then refined and classified into four categories， namely， normal， corroded， broken， and collision， to serve as the data basis for the algorithm research designed in this paper. The anti-shock hammer defect detection algorithm is then designed. The proposed anti-shock hammer defect detection method is mainly divided into two major parts， namely， the feature extraction network and classification location prediction network. The feature extraction network extracts accurate anti-seismic hammer features by fusing the Visual Geometry Group 16-layer network （VGG16）. The main idea is to insert a convolutional kernel of size 1 × 1 into the last convolutional layer of the first， third， and fifth layers， after which the last convolutional layer of the first layer is connected to the maximum pooling layer and fused with the third layer using a deconvolution operation after the fifth layer. These two layers are then fused to form the final feature map， which balances the semantic information and spatial features. To reduce the impact of the intersection over union （IoU） threshold on network performance， in the localization and classification network part， the classification and location prediction network proposed in this paper uses three cascade detectors to gradually increase the IoU threshold and improve the quality of samples and the training effect of the network. The non-maximal suppression method is then replaced by the soft non-maximum suppression （Soft-NMS） algorithm to remove the bounding box.
Result
2
The main contributions of this paper are as follows： 1） the dataset part expands the data using the aerial images taken via the UAV inspection of anti-vibration hammer components to establish the anti-vibration hammer defect detection dataset. To ensure the validity of this dataset， part of those samples that cannot be easily separated even through the naked eye are reasonably removed； 2） the network model is based on VGG16， feature fusion is performed on 1， 3， and 5 layers of features to effectively obtain more features， and three cascade detectors are used to classify the target to reduce the impact of the IoU threshold on network performance and improve the detection capability of the algorithm. The average accuracy of the network model was improved by 13.5%， 3.4%， and 5.8% over the fast region-based convolutional network （Fast R-CNN）， Faster R-CNN， and you only look once version 4 （YOLOv4）， respectively， and by 9.5%， 8.5%， and 8% over single shot MultiBox detector 300 （SSD300）， YOLOv3， and RetinaNet， respectively， when compared with six other advanced algorithms based on deep learning. These results may be explained by the incorporation of multiple layers of features， which enable the model to obtain more information about low- and high-level image features. Moreover， the uses of cascade detectors reduce the impact of the IoU thresholds on network performance and eventually improve the detection accuracy of small-scale targets. Compared with Faster R-CNN， the false detection rate of the proposed method is reduced by 5.61%， whereas the missed detection rate is reduced by 3.01%. Therefore， the proposed method improves its accuracy while effectively reducing its false detection and missed detection rates and works particularly well in practical applications.
Conclusion
2
The proposed anti-shock hammer defect detection method obtains good detection results for different backgrounds， illuminations， angles， scales， and types of anti-shock hammers. Experimental results show that this method not only efficiently extracts the characteristics of anti-shock hammers but also improves the localization accuracy of the network， thus effectively improving the accuracy of the algorithm and satisfying the actual detection requirements of anti-shock hammer inspection work while showing improved robustness and effectiveness.
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