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阎光伟, 周香君, 焦润海, 何慧(华北电力大学控制与计算机工程学院, 北京 100096)

摘 要
目的 螺栓是输电线路中数量最多的紧固件,一旦出现缺陷就会影响电力系统的稳定运行。针对螺栓缺陷自动检测中存在的类内多样性和类间相似性挑战,提出了一种融合先验信息和特征约束的Faster R-CNN(fasterregions with convolutional neural network)模型训练方法。方法 在航拍巡检图像预处理阶段,设计了基于先验信息的感兴趣区域提取算法,能够提取被识别目标的上下文区域,从而减少模型训练阶段的数据量,帮助模型在训练阶段关注重点区域,提高其特征提取能力。在模型训练阶段,首先通过费舍尔损失约束Faster R-CNN模型的输出特征生成,使样本特征具有较小的类内距离和较大的类间间隔;然后采用K近邻算法处理样本特征得到K近邻概率,将其作为难易样本的指示以引导模型后续更加关注难样本。结果 在真实航拍巡检图像构建的螺栓数据集上进行测试,与基线模型相比,本文模型使螺栓识别的平均精度均值(mean average precision,mAP)提高了6.4%,其中正常螺栓识别的平均精度(average precision,AP)提高了0.9%,缺陷螺栓识别的平均精度提高了12%。结论 提出的融合先验信息和特征约束的输电杆塔螺栓缺陷检测方法在缺陷螺栓识别上获得了良好的效果,为实现输电线路螺栓缺陷的自动检测奠定了良好的基础。
Defect detection of tower bolts by fusion of priori information and feature constraints

Yan Guangwei, Zhou Xiangjun, Jiao Runhai, He Hui(School of Control and Computer Engineering, North China Electric Power University, Beijing 100096, China)

Objective Bolts,as fasteners,play a role in connecting and fixing key components in power towers. Adverse environments,mechanical vibrations,and material aging may cause the bolt cotter pins to fall off or come out,thus impacting the normal operations of other parts on the transmission line. Monitoring,testing,and maintenance of bolt conditions play crucial roles in ensuring the uninterrupted operation of a power system. However,the timely and efficient automatic detection of transmission line bolt defects in a power system poses a challenge. Aiming at the challenges of intra-class diversity and inter-class similarity in automatic bolt defect detection,a faster regions with convolutional neural network(Faster R-CNN)model training method based on prior information and feature constraints is proposed in this paper. Method In the pre-processing of an aerial inspection image,this paper designs a region of interest extraction algorithm based on prior information that can extract the context region of the identified object so as to reduce the amount of data in the training stage,help the model focus on the key areas in the training stage,and improve its feature extraction ability. In the model training stage,the output features of Faster R-CNN model are initially constrained by the Fisher constraint to reduce the intra-class distances of the sample features while increasing their inter-class intervals,which improves the differentiation of sample features. Afterward,the K-nearest neighbor algorithm is used to process the sample features to obtain the k-nearest neighbor probability,which is used as an indication of difficult samples to make the model pay attention to difficult samples in the future. Result All data used in this paper are real aerial inspection images from a power company in Central China. Bolts on the power tower can be divided into bolts with and without pins. Bolts with pins connect and fix the key components and need to bear a large force. Compared with bolts without pins,those with pins are more prone to defects. Therefore,this paper treats bolts with pins as the research object. According to the state of the cotter pin on the bolt,bolts with pins are divided into normal and defective bolts,of which defective bolts have fallen or loose cotter pins. The dataset contains 28 887 images,with each image having a resolution of 5 000×3 000 pixels. The training set,validation set,and test set are divided according to the ratio of 8∶1∶1. The proposed model is then tested on this dataset. Compared with the baseline model,the proposed model improves the mean average precision of bolt recognition by 6. 4%,increases the average precision of normal bolt identification by 0. 9%,and increases the average accuracy of defective bolt identification by 12%. Conclusion In this paper,the problems of intra-class diversity and inter-class similarity in bolt defect detection are explored ,and a method of transmission tower bolt defect detection based on the fusion of prior information and feature constraints is proposed. This improves the recognition effect of the model on bolt defects and lays a good foundation for the automatic detection of bolt defects in transmission lines.