输电线路中典型金具及其缺陷的检测是非常重要的巡检任务。针对由于金具尺度变化大且部分金具为小尺度目标进而导致金具检测精确度低的问题，提出了一种基于改进Cascade R-CNN的典型金具及其部分缺陷检测方法。在Cascade R-CNN模型的基础上，采用递归特征金字塔结构进行特征优化，纵向优化层级高级语义特征，横向反馈连接增益主干网络特征图；同时提出使用NAS（neural architecture search）获取空洞卷积的孔洞率来扩大感受野的方式使卷积对多尺度金具特征提取更有效。实验结果证明：提出的递归特征金字塔与NAS搜索孔洞率的空洞卷积相结合改进Cascade R-CNN的方法，在一定程度上解决了金具检测精确度低的问题。其中性能指标AP（average precision）值提高了6.72%，最高检测精确度达到了92.34%。该研究为进一步对典型金具进行故障诊断，实现智能巡检奠定了良好的基础。

针对现有的盲图像质量评价算法在面对真实失真图像时性能较低的情况，本文提出多层特征融合和语义信息增强相结合的无参考图像质量评价算法（multi-level feature fusion and semantic enhancement for NR，MFFSE-NR）。提取图像的局部和全局失真特征，利用特征融合模块对特征进行多层融合；利用多层扩张卷积增强语义信息，进而指导失真图像到质量分数的映射过程；考虑预测分数和主观分数之间的相对排名关系，对L1损失函数和三元组排名损失函数进行融合，构建新的损失函数Lmix。为了验证本文方法的有效性，在LIVEC数据集上进行了验证和对比实验，该算法的SROCC与PLCC指标相比原算法分别提升2.3%和2.3%；在KonIQ-10k数据集和LIVEC数据集上进行了跨数据集实验，该算法在面对真实失真图像时表现出了良好的泛化性能。

为了实现薄板自动对接焊过程中焊缝中心的准确检测，提出了基于主动视觉传感的薄板对接焊缝检测方法。通过对工业相机采集的图像进行颜色分割与亮度分割，消除环境光的干扰；利用轮廓检测与掩模操作消除图像噪声，应用灰度重心法提取激光条纹的中心线，并通过一阶差分方法实现焊缝左右边界及中心的检测。针对激光条纹存在的分断缺陷进行续断连接，以提高焊缝中心检测的准确性。实验结果表明，采用所提方法能够准确检测焊缝边界及中心位置，能够满足薄板对接焊缝的自动焊接需求。

为了降低影响并提高对变压器渗漏油巡检图像的检测效率，提出一种基于深度可分离空洞卷积金字塔的变压器渗漏油检测模型。首先，将空洞金字塔中普通卷积块修改为深度可分离卷积块，以此扩大金字塔感受野，使特征提取网络提取到的特征图语义信息更加丰富；然后，改进了特征提取阶段低阶语义特征与高阶语义特征融合过程，进一步增强特征提取网络产生特征图的语义信息；最后，为了避免经过多次卷积、池化操作后特征图语义信息的损失，在融合过程中引入空间注意力机制和通道注意力机制，进一步增强特征图中的语义信息。与UNet（Convolutional Networks for Biomedical Image Segmentation）、PSPNet（Pyramid Scene Parseing Network）、DeepLabv3+（Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation）和MCNN（Multi-Class Convolutional Neural Network）等算法进行对比实验发现，本文所提出网络检测模型效果好，查准率达到了76.85%，平均交并比达到了64.63%，召回率达到了73.56%，FPS（Frames Per Second）达到了30帧/秒。为了验证本文提出方法的有效性，设计了消融实验，与基础网络模型相比，查准率提高了9.33%，平均交并比提高了7.15%，召回率提高了5.66%。

Insufficient sample and unbalanced sample are problems for disc insulator defect detection. On the one hand, to solve the problem of insufficient sample, a transfer learning method based on mixed sample is developed for disc insulator defect detection which uses parallel vision theory as a framework. Two artificial samples of insulator dropped and damaged are generated by fusing disc insulator prior knowledge and rule criteria. On the other hand, to solve the problem of unbalanced sample, a balance loss function is proposed from the perspective of sample balance. During the training process, the model assigns different weights to positive sample, negative sample, easy sample and difficult sample. Using this method, the attention of the model to positive samples, especially the ones that are difficult to detect, is improved. The experimental results show that the accuracy of the proposed method for disc insulator defect detection reaches 75.1% and the recall reaches 81.7% in the case of small sample, which are 19.2% and 9.2% improvement compared with the original algorithm, providing a new solution and an implementation method to the problem of defect detection with small samples. ©2023 Chin.Soc.for Elec.Eng.

螺栓缺陷非常容易引起输电线路异常甚至故障,但大量的缺陷数据难以获得。将生成式对抗网络应用于缺陷螺栓图像的生成,针对生成过程中存在的图像质量差、生成样本单一,模型收敛缓慢等问题,提出一种基于改进DCGAN的螺栓图像生成方法。在损失函数中加入相对均值鉴别器和梯度惩罚,平衡了生成器和判别器的能力,提高了样本质量和模型的收敛速度;在模型的生成器和鉴别器中引入注意力机制,捕获图像中长距离的像素特征,提高了缺陷样本的多样性;实验结果验证了改进方法的有效性,IS值提高了0. 1,实现了缺陷样本的扩增。

Due to the small sample size and unbalanced distribution, fault recognition of substation equipment becomes difficult. A few-shot feature enhancement method is proposed and applied on hybrid sampling feature enhancement model. First, Gaussian sampling structure and Gumbel-SoftMax sampling structure are designed based on the constructed intra-class covariance matrix which represents the semantic direction. After that, Hybrid sampling feature enhancement (HSFE) model is proposed, and the loss upper bound of the model is deduced. Finally, those proposed design methods can be implemented. Experiments confirm the effectiveness and improvement of fault recognition rate for substation equipment compared to existing approaches, especially the value of mAP is 4% higher than the baseline model. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

针对盘型绝缘子缺陷检测过程中存在样本不足和样本不平衡的问题，一方面以平行视觉理论为框架，融合盘型绝缘子先验知识和规则标准，生成盘型绝缘子伞盘脱落和破损两种人工样本，建立以混合样本为基础的迁移学习方法，解决样本不足的问题；另一方面，从样本均衡的角度出发，提出均衡损失函数，在训练过程中分别对正负样本和难易样本分配不同的权重，提高模型对正样本尤其是难以检测样本的关注度，解决正负样本、难易样本不平衡的问题。实验结果表明，在少样本情况下，提出的方法用于盘型绝缘子缺陷检测的准确率达到75.1%，召回率达到81.7%，相比原始的算法分别提升19.2%和9.2%，结果可为少样本缺陷检测问题提供了新的解决思路与实现方法。

Currently, reliable and accurate object detection in high-resolution remote sensing images still faces significant challenges, such as color, aspect ratio, complex background, and scale variations. Even the detection results obtained based on the latest convolutional neural network (CNN) methods are not satisfactory. To obtain more accurate detection results from large-scale remote sensing images, we proposed a multiscale object detection algorithm oriented to adaptively attentional feature fusion based on the YOLOX algorithm. First, a multiscale attentional feature fusion (MSAFF) structure was added to the YOLOX network to increase the perceptual field and aggregate the contextual information to obtain a feature map with richer semantic information. Second, an adaptively spatial feature fusion (ASFF) structure was introduced to process the fused feature maps, to which spatial feature weights were assigned at different levels to enhance the feature representation of remote sensing objects and reduce feature loss. Finally, the aligned convolutional (Align Conv) network was used in the object classification and localization task to achieve a more accurate localization of densely arranged objects with arbitrary orientation. The algorithm proposed in this article was extensively experimented on the PASCAL VOC and the DIOR datasets, and the average accuracy reached 89.2% and 75.3%, respectively. Compared with some current mainstream two-stage and one-stage object detection algorithms, the experimental results demonstrated that our method performed well in both accuracy and speed. At the same time, it reduced the leakage rate of remote sensing objects to a certain extent.

针对遥感图像中感兴趣目标特征不明显、背景信息复杂、小目标居多导致的目标检测精度较低的问题，本文提出了一种改进YOLOv5s的遥感图像目标检测算法（Swin-YOLOv5s）。首先，在骨干特征提取网络的卷积块中加入轻量级通道注意力结构，抑制无关信息的干扰；其次，在多尺度特征融合的基础上进行跨尺度连接和上下文信息加权操作来加强待检测目标的特征提取，将融合后的特征图组成新的特征金字塔；最后，在特征融合的过程中引入Swin Transformer网络结构和坐标注意力机制，进一步增强小目标的语义信息和全局感知能力。将本文提出的算法在DOTA数据集和RSOD数据集上进行消融实验,结果表明,本文提出的算法能够明显提高遥感图像目标检测的平均准确率。