全局上下文增强的稀疏卷积电网防外力破坏检测

Sparse Convolutional Network with Global Context Enhancement for Anti-external Force Damage Detection of Power Grid

在输电线路防外力破坏巡检场景中,当前部署于边缘端的轻量级目标检测算法,存在检测精度不足、推理速度慢等问题.针对以上问题,本文提出一种基于全局上下文增强的稀疏卷积电网防外力破坏检测算法Fast-YOLOv5.基于YOLOv5算法,设计了FasterNet+网络作为新的特征提取网络,在保持检测精度的同时,提升模型的推理速度,并降低计算复杂度;在算法的瓶颈层中,设计了具有高效通道注意力的ECAFN模块,通过自适应地校准通道方向上的特征响应,高效获取跨通道的交互信息来提升检测效果,并进一步减少参数量和计算量;提出了具有上下文增强的稀疏卷积网络SCN替换模型的检测层,通过捕获全局上下文信息来增强前景焦点特征,提高模型的预测能力.实验结果表明,改进后的模型与原模型相比,精度提升了1.9%,检测速度提升了1倍,达到56.2 f/s,参数量和计算量分别下降了50%和53%,更符合输电线路高效检测的要求.

In the anti-external force damage inspection of transmission lines,the current lightweight target detection algorithm deployed at the edge has insufficient detection accuracy and slow reasoning speed.To solve the above problems,this study proposes a sparse convolution network(SCN) with global context enhancement for anti-external force damage detection of the power grid,Fast-YOLOv5.Based on the YOLOv5 algorithm,the FasterNet+ network is designed as a new feature extraction network,which can maintain detection accuracy,improve the reasoning speed of the model,and reduce computational complexity.In the bottleneck layer of the algorithm,an ECAFN module with efficient channel attention is designed,which improves the detection effect by adaptively calibrating the feature response in the channel direction,efficiently obtaining the cross-channel interactive information and further reducing the amount of parameters and calculation.The study proposes the detection layer of the sparse convolutional network SCN replacement model with context enhancement to enhance the foreground focus feature and improve the prediction ability of the model by capturing the global context information.The experimental results show that compared with the original model,the accuracy of the improved model is increased by 1.9%,and the detection speed is doubled,reaching 56.2 f/s.The amount of parameters and calculation are reduced by 50% and 53% respectively,which is more in line with the requirements for efficient detection of transmission lines.