摘要：

在电力系统中，输电线路的稳定性和可靠性至关重要，其中螺栓作为连接和固定线路主体的关键组件，对维持电力系统的稳定性起着决定性的作用。然而，在进行输电线路的巡检时，由于螺栓在巡检图像中所占的比例较小、分布不均且其特征不明显，利用视觉方法来检测这些螺栓的缺陷变得尤为困难。针对这些问题，提出了自适应分块检测方法，该方法包含两个阶段：第一阶段首先采用改进的目标密度分布图生成网络预测出包含目标大致尺寸以及分布信息的目标密度分布图，该网络是由基于参数重构和多维动态卷积技术的RepODconv卷积块组成，有效控制模型的参数量，同时增强对小尺寸目标的关注能力，接着根据该目标密度分布图采用设计的聚类分块算法得到固定尺寸且未缩放的分块区域图像；第二阶段采用结合自注意力模块的YOLOX模型对这些图像进行检测，提升网络对不同类别缺陷的鉴别能力。在无人机巡检的输电线路螺栓数据集上进行实验，所提方法对多数类别缺陷的召回率、准确率均达到70%，相比于目前先进的检测网络的实验结果，交并比(IoU)为0.5的平均精度(mAP@0.5)提升约30%，平均精度(mAP)提升约70%，特别是小目标的mAP提升约2倍。

关键词: 输电线螺栓, 小目标检测, 自适应分块检测, 缺陷检测系统, 参数重构, 多维动态卷积, 注意力机制

Abstract:

In power systems, the stability and reliability of transmission lines are crucial. Bolts, as key components for connecting and fixing the main body of the lines, play a decisive role in maintaining the stability of a power system. However, during the inspection of transmission lines, detecting defects in these bolts using vision-based methods becomes particularly difficult because of the small proportion, uneven distribution, and indistinct features of bolts in the inspection images. To address these issues, an adaptive block detection method consisting of two stages is designed. In the first stage, an improved target density distribution map generation network is employed to predict a target density distribution map containing the approximate size and distribution information of the targets. This network is composed of RepODconv convolution blocks based on parameter reconstruction and multidimensional dynamic convolution technology, which effectively controls the model′s parameter quantity while enhancing the network′s attention to small-sized targets. Subsequently, a clustering block algorithm is designed to obtain fixed-size and unscaled block-area images based on this target density distribution map. In the second stage, the YOLOX model combined with self-attention modules is employed to detect these images, enhancing the network′s discrimination ability for defects of different categories. Experimental results on a dataset of transmission line bolt inspections by unmanned aerial vehicles show that the recall rate and precision of majority-class defects reach 70%. Compared to the experimental results of current advanced detection networks, the Average Precision at Intersection over Union (IoU) of 0.5 (mAP@0.5) is improved by approximately 30%, mean Average Precision (mAP) is improved by approximately 70%, and the mAP of small targets is improved by approximately 2 times.

Key words: transmission line bolts, small target detection, adaptive block detection, defect detection system, parameter refactoring, multi-dimensional dynamic convolution, attention mechanisms