基于YOLOv5与ORB-SLAM融合的变电站动态场景特征点筛选算法改进

doi:10.3969/j.issn.2097-0706.2026.02.005

综合智慧能源 ›› 2026, Vol. 48 ›› Issue (2): 47-58.doi: 10.3969/j.issn.2097-0706.2026.02.005

• 人工智能赋能的运维与巡检 • 上一篇下一篇

基于YOLOv5与ORB-SLAM融合的变电站动态场景特征点筛选算法改进

何龙庆¹(), 李小勇²(), 石鑫²(), 姜寒³(), 李玉强⁴(), 王永君⁴(), 王凯¹^,^*()

1.青岛大学电气工程学院，山东青岛 266071
2.山东广域科技有限责任公司，山东东营 257029
3.国家电投东北电力有限公司抚顺热电分公司，辽宁抚顺 113010
4.青岛海尔智能技术研发有限公司，山东青岛 266101

收稿日期:2025-09-15 修回日期:2025-11-30 出版日期:2026-02-25
通讯作者: *王凯（1985），男，教授，博士生导师，博士，从事新型电力系统智能控制与安全防御、新能源储能器件状态评估和寿命预测、储能元件、新能源的存储和转化、能源互联网等方面的研究，wkwj888@163.com。
作者简介:何龙庆（2002），男，硕士生，从事变电站智能巡检、机器视觉等方面的研究，hlq20011225@163.com；
李小勇（1983），男，工程师，从事110 kV变电站安装工程方面的研究，275314252@qq.com；
石鑫（1987），男，工程师，从事110 kV变电站安装工程方面的研究，372300812@qq.com；
姜寒（2000），男，工程师，硕士，从事变电站巡检、机器视觉等方面的研究，1026172648@qq.com；
李玉强（1985），男，工程师，从事传感器检测技术、人工智能技术在智能家电的应用等方面的研究，liyuqiang920@163.com；
王永君（1981），男，工程师，从事传感器应用、家电智能控制等方面的研究，thywyj@126.com。
基金资助:
中央引导地方科技发展资金项目(YDZX2024060)

Improvement of feature point filtering algorithm for dynamic scenarios in substations based on fusion of YOLOv5 and ORB-SLAM

HE Longqing¹(), LI Xiaoyong²(), SHI Xin²(), JIANG Han³(), LI Yuqiang⁴(), WANG Yongjun⁴(), WANG Kai¹^,^*()

1. College of Electrical Engineering， Qingdao University， Qingdao 266071， China
2. Shandong Guangyu Technology Company Limited， Dongying 257029， China
3. Fushun Thermal Power Branch， State Power Investment Corporation Northeast Electric Power Company Limited， Fushun 113010， China
4. Qingdao Haier Intelligent Technology Research and Development Company Limited， Qingdao 266101， China

Received:2025-09-15 Revised:2025-11-30 Published:2026-02-25
Supported by:
Central Guidance Funds for Local Science and Technology Development Projects(YDZX2024060)

摘要/Abstract

摘要：

针对变电站复杂动态工况下智能巡检机器人定位建图精度衰减问题，提出一种融合改进CA-YOLOv5目标检测的增强型定位与地图构建架构。采用多模态注意力机制优化CA-YOLOv5网络，构建动态目标实时识别框架；通过语义-几何联合约束策略，在特征匹配阶段建立动态区域掩膜与运动概率模型；设计基于时空一致性的动态特征过滤算法，在捆绑调整优化环节实现动态干扰源的精准剔除与静态场景结构的有效保留。在公开数据集与真实动态场景中的对比试验表明，改进系统将动态环境下的定位误差降低43.7%，地图重建完整度提升41.5%，同时维持良好的实时处理性能。融合框架解决动态元素导致的误匹配与地图污染问题，有效克服了变电站典型动态干扰。

关键词: ORB-SLAM3, YOLO, 智能巡检, 传感器识别, 动态剔除, 变电站, 注意力机制

Abstract:

To address the degradation in localization and mapping accuracy of intelligent inspection robots under complex and dynamic operating conditions in substations，an enhanced localization and mapping architecture integrating improved CA-YOLOv5 target detection was proposed. A multimodal attention mechanism was used to optimize the CA-YOLOv5 network and construct a real-time dynamic target recognition framework. A semantic-geometric joint constraint strategy was used to establish a dynamic region mask and a motion probability model during the feature matching stage. A dynamic feature filtering algorithm based on spatiotemporal consistency was designed to achieve precise elimination of dynamic interference sources and the effective preservation of the static scene structure in the BA optimization process. Comparative experiments on public datasets and real dynamic scenarios demonstrated that the improved system reduced the localization errors by 43.7% and improved the map reconstruction completeness by 41.5% in dynamic environments，while maintaining satisfactory real-time processing performance. The fusion framework effectively solves the problems of mismatching and map pollution caused by dynamic elements，thereby overcoming typical dynamic disturbances in substations.

Key words: ORB-SLAM3, YOLO, intelligent inspection, sensor recognition, dynamic removal, substation, attention mechanism

中图分类号:

TM63：TP242

何龙庆, 李小勇, 石鑫, 姜寒, 李玉强, 王永君, 王凯. 基于YOLOv5与ORB-SLAM融合的变电站动态场景特征点筛选算法改进[J]. 综合智慧能源, 2026, 48(2): 47-58.

HE Longqing, LI Xiaoyong, SHI Xin, JIANG Han, LI Yuqiang, WANG Yongjun, WANG Kai. Improvement of feature point filtering algorithm for dynamic scenarios in substations based on fusion of YOLOv5 and ORB-SLAM[J]. Integrated Intelligent Energy, 2026, 48(2): 47-58.

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算法	RMSE	均值	中值	STD
ORB-SLAM3	0.622	0.573	0.483	0.241
YOLOv5-ORB-SLAM2	0.022	0.019	0.017	0.010
YOLOv5-ORB-SLAM3	0.023	0.021	0.019	0.009
YOLOv5-ORB-SLAM3 (Dense)	0.020	0.018	0.016	0.009

算法	RMSE	均值	中值	STD
ORB-SLAM3	0.027	0.021	0.016	0.016
YOLOv5-ORB-SLAM2	0.016	0.013	0.011	0.009
YOLOv5-ORB-SLAM3	0.015	0.012	0.010	0.009
YOLOv5-ORB-SLAM3(Dense)	0.014	0.011	0.010	0.008

模型	mAP@0.5/%	FPS	e_APE/m	评价
YOLOv8-ORB-SLAM3(Dense)	89.6	15.6	0.215	提升了检测精度，但结构复杂，推理速度较慢，实时性略有下降
YOLOv9-ORB-SLAM3(Dense)	90.2	16.2	0.205	精度进一步提升，FPS表现优于YOLOv8，但动态环境中的稳定性欠佳
YOLOv10-ORB-SLAM3(Dense)	91.3	16.4	0.198	轻量化优化显著，实时性更高，但在动态目标检测中的鲁棒性较差
YOLOv5-ORB-SLAM3（Dense）	92.4	16.8	0.189	精度最高，定位误差最低，鲁棒性最佳，算力消耗低，适合部署

基于YOLOv5与ORB-SLAM融合的变电站动态场景特征点筛选算法改进

Improvement of feature point filtering algorithm for dynamic scenarios in substations based on fusion of YOLOv5 and ORB-SLAM

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