一种EO-YOLOX输电线绝缘子检测方法

EO-YOLOX Model for Insulators Detection in Transmission Lines

为了保证电力系统的安全运行,使用无人机巡检技术对高压绝缘子进行日常检查是必要的。然而,受到电力线磁场和飞行安全的影响,图像数据中绝缘子像素表征减少,进而导致绝缘子检测的准确性降低。针对上述问题,提出了一种有效优化YOLOX(Efficient Optimization YOLOX,EO-YOLOX)检测模型。该模型首先利用空洞卷积(Atrous Convolution)的思想,提出了空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块,消除图像中的无关信息,提高了网络识别感兴趣区域的能力。其次,在特征融合阶段加入了注意特征融合(Attentione Feature Fusion,AFF)模块,通过向融合特征图中补充深层语义和浅层细节信息,提高了检测绝缘子的准确性。最后,针对传统损失函数不能准确反映两个边界框之间距离的问题,提出了一种优化损失函数,以更准确地评估边界框的质量。将该算法在绝缘子数据集上进行了实验和测试,结果表明,与传统的YOLOX方法相比,该算法在识别绝缘子方面表现优异,mAP值提高了约2.59%。该模型的实时处理效率高达41.21帧每秒,有效解决了绝缘子检测难题。

To ensure the safe operation of the power system,daily inspection of high voltage insulators using UAV inspection techniques is necessary.However,the influence of power line magnetic field and flight safety leads to a reduction of insulator pixel representation in the image data,which in turn reduces the accuracy of insulator detection.To address these issues,this paper proposes an efficient optimization YOLOX(EO-YOLOX)detection model.Firstly,the model makes use of the idea of atrous convolution and proposes the atrous spatial pyramid pooling(ASPP)module,which eliminates the irrelevant information in the image and improves the ability of the network to identify the region of interest.Secondly,the attentione feature fusion(AFF)module is added to the feature fusion stage,which improves the accuracy of detecting insulators by supplementing deep semantic and shallow detail information into the fused feature map.Finally,for the problem that the traditional loss function cannot accurately reflect the distance between two bounding boxes,this paper proposes an optimised loss function to more accurately assess the quality of the bounding boxes.Experiments and tests are carried out on the insulator dataset,and the experiment results show that the proposed algorithm performs excellently in identifying insulators,with an improvement of about 2.59% in mAP value,compared with the traditional YOLOX method.The real-time processing efficiency of the model is as high as 41.21 frames per second,which effectively solves the insulator detection problem.