| 谢静,龙志宏,刘志坚,段绍立,杜耀文,肖韩.云边端协作的架空线路鸟巢检测与定位算法研究[J].电子测量与仪器学报,2024,38(7):64-78 |
| 云边端协作的架空线路鸟巢检测与定位算法研究 |
| Bird’s nest detection and positioning algorithm of overhead line basedon cloud-edge-end collaboration system |
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| DOI： |
| 中文关键词: 云边端协作 架空线鸟巢 YOLOv5x 目标检测 目标定位 |
| 英文关键词:cloud side end collaboration overhead line bird’s nest YOLOv5x target detection objective positioning |
| 基金项目:云南省重点基金(202303AA080002)项目资助 |
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| Author | Institution |
| Xie Jing | Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China |
| Long Zhihong | Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China |
| Liu Zhijian | Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China |
| Duan Shaoli | Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China |
| Du Yaowen | Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China |
| Xiao Han | Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China |
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| 中文摘要: |
| 针对云端单一集中数据处理时效性低、架空线路上鸟巢检测精度不高、模型对边缘计算设备算力高消耗以及目标定位不准确的问题，提出了一种基于云边端协作的架空线路鸟巢检测与定位算法。该算法通过云、终、边缘3端的协作，解决了云端集中处理效率低的问题，并通过云边数据可视化协作解决由于角度及光线引起的图像不清晰问题。为了提高架空线路鸟巢检测的精度，该算法在YOLOv5x模型基础上进行了优化。首先，通过将主干特征提取网络中的C3模块替换为C2f模块，并在最后一层加入SE（squeeze and excitation）注意力模块，以提升模型对小目标的检测能力。其次，将激活函数替换为Mish函数，解决训练梯度饱和导致神经元停止学习的问题。为了降低模型对边缘计算设备算力的消耗，对改进后的模型进行剪枝微调以降低模型参数规模。基于此优化模型，提出了三维目标定位算法，结合GIS（geographic information system）系统对定位结果进行修正，实现了对检测目标的精准定位。实验数据显示，改进后的模型平均精度均值达到93.25%，比原YOLOv5x模型提升了3.44%，优化后的模型剪枝率达到45%。检测目标经过三维空间建模计算并通过位置修正能够定位到相应的杆塔，有效指导工作人员快速准确排除隐患。 |
| 英文摘要: |
| Aiming at the problems of low timeliness of single centralized data processing in the cloud, low accuracy of bird’s nest detection on overhead lines, high consumption of model’s arithmetic power on edge computing devices, and inaccurate target localization, an algorithm for detecting and localizing bird’s nests on overhead lines based on the collaboration of cloud-edge and end-end is proposed. The algorithm solves the problem of low efficiency of centralized processing in the cloud through the collaboration of cloud, end and edge, and solves the problem of unclear images due to angle and light through the collaboration of cloud-edge data visualization. In order to improve the accuracy of bird’s nest detection on overhead lines, the algorithm is optimized on the basis of YOLOv5x model. First, by replacing the C3 module in the backbone feature extraction network with the C2f module, and adding the SE (squeeze and excitation) attention module in the last layer to improve the model’s ability to detect small targets. Secondly, the activation function is replaced with the Mish function to solve the problem of neurons stopping learning due to the saturation of the training gradient. In order to reduce the model’s consumption of computing power on edge computing devices, the improved model is pruned and fine-tuned to reduce the scale of model parameters. Based on this optimized model, a 3D target localization algorithm is proposed, and the localization results are corrected by combining with the GIS (geographic information system) system, which achieves accurate localization of the detected target. The experimental data show that the mean average accuracy of the improved model reaches 93.25%, which is 3.44% higher than the original YOLOv5x model, and the pruning rate of the optimized model reaches 45%. The detection target is able to locate to the corresponding pole tower after 3D spatial modeling calculation and position correction, which effectively guides the staff to quickly and accurately eliminate hidden dangers. |
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