[1]赵文清,蔡建颖,李赛辰.基于阶梯式特征融合的输电线路外力破坏检测[J].智能系统学报,2025,20(5):1082-1092.[doi:10.11992/tis.202406045]
ZHAO Wenqing,CAI Jianying,LI Saichen.Detection of external force damage of transmission lines based on stepwise feature fusion[J].CAAI Transactions on Intelligent Systems,2025,20(5):1082-1092.[doi:10.11992/tis.202406045]
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ZHAO Wenqing,CAI Jianying,LI Saichen.Detection of external force damage of transmission lines based on stepwise feature fusion[J].CAAI Transactions on Intelligent Systems,2025,20(5):1082-1092.[doi:10.11992/tis.202406045]
基于阶梯式特征融合的输电线路外力破坏检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第5期
页码:
1082-1092
栏目:
学术论文—机器学习
出版日期:
2025-09-05
- Title:
- Detection of external force damage of transmission lines based on stepwise feature fusion
- Keywords:
- external force damage of transmission lines; object detection; feature extraction; stepwise feature fusion; lightweight head; GCIoU loss function
- 分类号:
- TP3-05
- 摘要:
- 针对输电线路巡检中无人机拍摄角度下器械易形变及带臂机械不同工作状态下特征复杂难以捕获的问题，提出一种基于阶梯式特征融合的外力破坏检测方法。该方法首先通过融合可变形大卷积核注意力网络提取无人机拍摄图像的特征信息；其次，利用多尺度序列特征融合模块进行阶梯式特征融合；然后，对检测头进行轻量化操作以减少参数量；最后，提出渐变完全交并比抑制(gradual complete intersection over union non-maximum suppression, GCIoU NMS)损失函数优化模型。在自建数据集上的实验表明，该方法的mAP50%和mAP50%-95%分别提高10.5和10.2百分点，达到86.8%和58.4%；在VOC数据集上，mAP50%和mAP50%-95%分别提高7.3和8.1百分点，达到79.5%和58.8%。实验结果表明，该方法有效提升了目标检测性能，对复杂环境下输电线路外部破坏检测具有重要参考价值。
- Abstract:
- A detection method based on stepwise feature fusion is proposed to address the challenges posed by instrument deformation under unmanned aerial vehicle (UAV) shooting angles and complex features caused by different operating states of arm-bearing machinery during transmission line inspections. The method integrates deformable large kernel attention networks to extract features from UAV images, performs stepwise feature fusion using a multiscale sequence feature fusion module, and applies lightweight operations to the detection head to reduce the parameters. An gradual complete intersection over union non-max-imum suppression(GCIoU NMS) loss function further enhances the model. Experiments on a self-constructed dataset show mAP50% and mAP50%-95% improvements of 10.5 and 10.2 points, reaching 86.8% and 58.4%, respectively. On the VOC dataset, mAP50% and mAP50%-95% increased by 7.3 and 8.1 points to 79.5% and 58.8%, respectively. The results demonstrate the effectiveness of the method for external force damage detection of transmission lines in complex environments.
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备注/Memo
收稿日期:2024-6-27。
基金项目:国家自然科学基金项目 (62371188); 河北省自然科学基金项目(F2021502013).
作者简介:赵文清，教授，博士，主要研究方向为人工智能与图像处理。获河北省科技进步二等奖、三等奖各1项。发表学术论文 50 余篇。E-mail：zhaowenqing@ncepu.edu.cn。;蔡建颖，硕士研究生，主要研究方向为图像目标检测。E-mail：937709507@qq.com。;李赛辰，硕士研究生，主要研究方向为图像目标检测。E-mail：WantedManOrz@outlook.com。
通讯作者:赵文清. E-mail：zhaowenqing@ncepu.edu.cn
更新日期/Last Update:
2025-09-05