基于两阶段深度网络的输电线路异常目标检测方法

Transmission line abnormal object detection method based on deep network of two-stage

输电线路的异常目标检测对提高输电系统的安全性、可靠性、稳定性起到十分重要的作用,而已有目标检测并未针对线路异常目标的尺度变化大、小目标多、光线暗、部分遮挡等问题进行有效设计,导致识别速度慢、易受环境干扰、误报漏报频发等.针对上述问题,采用两阶段深度网络,利用特征金字塔网络(FPN)提取多尺度特征,使主干网更好地适应目标多尺度变化,并通过全局网络进行特征增强,获得更清晰、更具有代表性的多尺度目标特征.在区域选择网络(RPN)中提出特征指导的候选框生成网络,能够生成稀疏且形状任意的锚,产生更紧密的掩模包围框.在检测阶段,采用多任务损失函数提升网络的预测精度和泛化能力,提高异常目标的检测性能.在MS COCO数据集上进行消融实验和性能对比,验证所提出方法的有效性和先进性,在输电线路数据集上异常目标检测精度达到77%,优于主流深度学习的目标检测方法.

Abnormal object detection of transmission lines plays a very important role in improving the safety,reliability and stability of transmission systems,but existing object detection has not been effectively designed for large scale changes,many small objects,dark light,partial occlusion of abnormal objects on the line,resulting that recognition speed is slow,it is easy to be disturbed by the environment,and false positives and false negatives are frequent.In response to the above problems,this paper adopts a two-stage deep network.The feature Pyiamid network(FPN)is used to extract multi-scale features so that the backbone network can better adapt to multi-scale changes of objects,and feature enhancement is performed through the global network to obtain clearer and representative multi-scale object features.A feature-guided region proposal generation network is proposed in the region proposal network(RPN),which can generate sparse and arbitrary-shaped anchors to generate tighter mask bounding boxes.In the detection stage,a multi-task loss function is used to improve prediction accuracy and generalization ability of the network,and to improve detection performance of abnormal objects.An ablation experiment and performance comparison on the MS COCO dataset verify the effectiveness and advancement of the proposed method.The detection accuracy of abnormal objects on the transmission line dataset reaches 77%,which is better than mainstream deep learning object detection methods.