| 王宏超,邵云峰,马中静,马治中,郭 星.基于深度学习增强的 LSD 杆塔倾斜度检测[J].电子测量与仪器学报,2021,35(9):204-213 |
| 基于深度学习增强的 LSD 杆塔倾斜度检测 |
| Tower tilt detection based on LSD enhancement by deep learning |
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| DOI： |
| 中文关键词: 杆塔倾斜度 YOLOv3 LSD 杆塔中线 |
| 英文关键词:power pole tower inclination YOLOv3 LSD pole tower axis |
| 基金项目:国网山西省电力公司科技项目(2400/2019 15004A)资助 |
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| 中文摘要: |
| 杆塔的倾倒会对整个电网产生严重的破坏并威胁周围居民的生命安全,电力巡检无人机利用计算机视觉对杆塔进行巡
检既节省了人力资源又显著地提高了电网的巡检效率。 为了国网巡检人员在杆塔倾倒前及时得到预警,对电力巡检无人机中
的基于计算机视觉的杆塔倾斜检测算法进行了研究,设计了一种基于 YOLOv3 的深度神经网络结合 LSD 线段提取方法对杆塔
的倾斜进行检测。 利用在山西电网无人机实际巡检的杆塔图片制作杆塔的 VOC2007 数据集并利用 YOLOv3 神经网络对杆塔
进行目标检测,并将检测后得到的 Bounding box 根据网络训练后的 mIOU 参数进行微调后作为 LSD 检测的 ROI。 接着,该方法
在 ROI 中将检测的线段进行过滤和融合,根据杆塔特点进行杆塔的二次识别。 最后利用得到的杆塔外边线做出该方向上的杆
塔中线并计算杆塔在该方向的倾斜度。 该文中实验利用山西国网电力公司提供的数据进行验证,结果表明,杆塔的倾斜检测效
果在各种拍摄高度和背景干扰下都较为精确,杆塔目标的正确识别率达到 97%,倾斜度检测平均误差小于 0. 85°。 |
| 英文摘要: |
| The tilt of the tower will cause serious damage to the entire power grid and threaten the lives of surrounding residents. The
power inspection performed by the computer vision of the UAVs not only saves labour, but also significantly improves the inspection
efficiency of the power grid. In order to get early warning before the tower falls for State Grid inspectors. In this paper, the algorithm of
computer vision-based tower tilt detection in electric patrol unmanned aerial vehicles is researched. And the tilt of tower is detected using
YOLOv3’s deep neural network combined with LSD line segment extraction method. Using the pole pictures of the actual inspection of
the UAVs in Shanxi power grid to make the VOC2007 dataset of the pole tower and use the YOLOv3 neural network to detect the pole
tower. The Bounding box obtained after the detection is fine-tuned according to the mIOU parameters after network training and used as
LSD detection ROI, the detected line segment is filtered and fused, and the secondary identification of the tower is performed according
to the characteristics of the tower. Finally, the outer line of the tower is used to make the center line of the tower in this direction and the
inclination of the tower in this direction is calculated. The experiment uses the data provided by Shanxi State Grid Electric Power
Company for verification. The tilt detection effect of the tower is more accurate under various backgrounds, and the accuracy and
environmental adaptability are significantly improved compared with other algorithms. The correct recognition rate of the tower target
reaches 97%, and the average error of the inclination detection is less than 0. 85°. |
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