基于边缘智能的输电线路异常目标高效检测方法研究

Research on Efficient Detection Technology of Transmission Line Abnormal Target Based on Edge Intelligence

输电线路异常目标所导致的故障已经成为造成输电线路停运的主要原因,对社会造成极大的经济损失。然而由于输电线路边缘侧设备算力、供电、通信资源受限,仍需将图像定时(如间隔30min)发送至数据中心进行处理,导致数据中心负荷重,检测漏报率高,应急处理能力严重不足。为此,文章构建了基于边缘智能的输电线路异常目标检测架构,并提出一种针对输电线路场景复杂背景复杂形状的异常目标检测模型,该模型基于改进型MobileNetv3网络精细化提取异常目标特征信息,然后利用多尺度目标检测网络YOLOv3融合高低维特征信息提升识别精度。进一步地,针对资源受限的边缘终端,基于贡献度感知通道剪枝策略,实现模型轻量化压缩,得到轻量化输电线路异常目标检测模型。最后,为了验证文章所提模型性能,与系列典型边缘侧模型进行对比实验,实验结果表明文章模型识别精度、推理速度均取得优异表现,并具备良好的泛化性和鲁棒性,实现了输电线路异常目标边缘侧高效检测。

The fault caused by abnormal targets has become one of the main reasons for the outage of transmission line,resulting in great economic losses.However,due to the limited computing,communication and power resources of transmission line edge equipment,the image still needs to be sent to the data center continually(at a rate of 2 times/h)for processing,resulting in heavy load on the cloud side,high omission factor and shortage of emergency processing capacity.As a result,an abnormal target detection architecture of transmission line based on edge intelligence was developed in this paper,and an abnormal target detection model for complex background and complex shape of transmission line scene was proposed.The feature information of abnormal targets is firstly extracted with the modified MobleNetv3 network.The multi-scale target detection network YOLOv3 is then implemented to fuse high and low-dimensional feature information,benefiting the detection of abnormal targets in complex backgrounds and/or with complex shapes.Finally,an ultra-lightweight monitoring model for abnormal targets of transmission lines can be obtained based on the importance channel pruning strategy.In order to verify the performance of the proposed model,comparative experiments were carried out with a series of existing models basing on an edge device for power IoT.The experimental results have demonstrated the recognition accuracy and inference speed of the model.The proposed model has been validated to be robust and flexible,which meets the need of edge detection for transmission line abnormal targets.