基于改进YOLOX的输电线路故障检测算法

Transmission Line Faults Detection Algorithm Based on YOLOX

电力系统是国民生活的重要基础,对输电线故障进行智能检测具有重大的社会和经济价值。针对输电线故障检测场景缺少公开数据集,同时存在多个尺度目标时检测效果差、高IoU检测框难以获取等问题,本文提出一种基于YOLOX的输电线故障检测算法。本文通过采集和仿真建立输电线故障检测数据集,然后在YOLOX特征融合机制的基础上,提出基于空洞卷积的自适应多尺度特征融合方法,实现多尺度特征的更有效利用,最后提出一种新的损失函数,可以有效提高网络对高IoU检测框的优化能力并解决样本不平衡问题,显著提高检测精度。实验结果表明,在本文的数据集中,本文所提的算法在保证实时性的同时,mAP_(50:95)依然能达到67.48%,超过了EfficientDet、YOLOV5等经典算法。

Power system is an important foundation of national life,intelligent detection of transmission line faults has great social and economic value.Aiming at the problem of lack of public datasets in transmission line faults detection scenarios,poor performance when there are multiple scale targets simultaneously,and difficulty in detecting high IoU bounding boxes,a transmission line faults detection method based on improved YOLOX was proposed.First,a transmission line faults detection dataset was set up through acquisition and simulation;then an adaptive multi-scale feature fusion method was proposed to fully use multiscale features;finally a new loss was proposed to improve the optimization ability of the network for high IoU bounding boxes and solve sample imbalance problem,which effectively improved the detection accuracy.The experimental results show that in the dataset collected in this paper,the proposed algorithm can still achieve 67.48%mAP_(50:95)while ensuring real-time performance,outperforming the classical algorithms such as EfficientDet and YOLOV5.