基于级联查询-位置关系的输电线路多金具检测方法  

Multi-fitting detection for transmission lines based on a cascade query-position relationship method

作　　者：翟永杰[1] 王璐瑶 赵晓瑜 胡哲东 王乾铭 王亚茹 ZHAI Yongjie;WANG Luyao;ZHAO Xiaoyu;HU Zhedong;WANG Qianming;WANG Yaru(Department of Automation,Huabei Electric Power University,Baoding Hebei 071003,China)

机构地区：[1]华北电力大学自动化系,河北保定071003

出　　处：《图学学报》2025年第2期288-299,共12页Journal of Graphics

基　　金：国家自然科学基金(U21A20486);河北省自然科学基金(F2021502008);中央高校基本科研业务费专项资金(2023JC006,2024MS136)。

摘　　要：针对输电线路航拍图像的金具目标尺寸小与密集遮挡问题,提出了基于级联查询-位置关系的输电线路多金具检测方法(CQPR)。首先提出了级联稀疏查询模块,通过小尺度特征图上小目标的粗略位置来查询大尺度特征图中的小目标精确位置,提高小目标金具检测的准确性。接着,提出了位置特征关系模块(PRM),通过利用图像中不同金具之间的位置关系建立PRM,提取金具位置关系,丰富遮挡区域的特征,进而优化了密集遮挡下的金具检测效果。多个基线模型上的实验结果表明,将CQPR应用到基线检测框架时,Faster R-CNN,Cascade R-CNN,Libra R-CNN和Dynamic R-CNN的准确率分别达到82.9%,82.4%,83.7%和77.3%,优于其他先进目标检测模型,对其中小目标金具和存在遮挡情况的金具检测准确率的提高较为明显,推理速度也有一定的提高,同时兼顾定位精度与检测实时性。To address the challenges posed by the small-size and dense occlusion of fittings in aerial images of transmission lines,a multi-fitting detection method for transmission lines based on a cascade query-position relationship(CQPR)was proposed.Firstly,a cascade sparse query module was proposed to query the precise location of small-size objects in large-scale feature maps using the rough location of small-size objects on small-scale feature maps,thereby improving the accuracy of small-size fitting detection.Then,a positional feature relationship module(PRM)was proposed to optimize the detection results at the occlusion using the positional relationships between different fittings in the image to establish the positional PRM.This enriched the features of occluded areas and optimized fitting detection performance under conditions of dense occlusion.Experimental results on multiple baselines demonstrated that when the proposed CQPR for mult-fitting detection in transmission lines was applied to the baseline,achieving the accuracy of Faster R-CNN,Cascade R-CNN,Libra R-CNN,and Dynamic R-CNN at 82.9%,82.4%,83.7%,and 77.3%,respectively.These results surpassed those of other state-of-the-art object detection models,particularly in the accuracy of detection for small-size fittings and fittings with occlusion.The inference speed was also improved,achieving a balance between localization accuracy and real-time detection.

关 键 词：输电线路 金具 深度学习 目标检测 小目标 密集遮挡 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TM75[自动化与计算机技术—计算机科学与技术]