基于多尺度特征编码和双重注意力融合的绝缘子缺陷检测

Insulator Defect Detection Based on Multi-Scale Feature Coding and Dual Attention Fusion

针对输电线路绝缘子缺陷检测准确率低和检测速度慢的问题,提出了一种基于多尺度特征编码和双重注意力融合的输电线路绝缘子缺陷检测方法。首先,为了使检测模型适应缺陷绝缘子特征尺度的多样性,编码网络采用Res2Net50提取更细粒度的特征,并在之后嵌入空洞空间金字塔池化模块实现多个尺度捕捉绝缘子及其缺陷的特征;其次,为了减少解码网络中特征信息的缺失,将主干网络的不同层特征与efficient channel attention注意力模块串联,并分别与经过squeeze and excitation注意力模块的各反卷积特征相加形成双重注意力融合。实验结果表明,所提方法的均值平均精度值约为95.35%,每秒传输帧数约为65.95,与其他方法相比,该方法对无人机绝缘子缺陷的准确检测具有一定的参考价值。

Aiming at the problem of low detection accuracy and slow detection speed of insulator defects in transmission lines,a defect detection method for transmission line insulators based on multi-scale feature coding and double attention fusion is proposed.First,in order to adapt the detection model to the diversity of characteristic scales of defective insulators,the coding network uses Res2Net50 to extract more fine-grained features,and then embeds the atrous spatial pyramid pooling structure to capture the characteristics of insulators and their defects at multiple scales.Second,in order to reduce the lack of feature information in the decoding network,The different feature layers of the backbone network are connected in series with the efficient channel attention attention module,and they are added to the deconvolution features of the squeeze and excitation attention module to form a double attention fusion.Finally,Experiments show that the mean average precision index of the proposed method reaches about 95.35%,and the frames per second reaches about 65.95,and compared with other algorithms,this method has certain reference value for realizing the accurate detection of insulator defects of unmanned aerial vehicles.