摘要：

输电铁塔作为输电线路的支撑, 其良好的状态对电力系统运行至关重要, 但目前研究中尚无输电铁塔状态检测数据集。为实现对铁塔危险状态的检测和预警, 首先构建一个包含异物入侵、动物巢穴、塔基遮挡、外力破坏等7种类别的铁塔状态图像数据集, 然后提出轻量的输电铁塔状态检测算法CT-YOLO。提出轻量化的骨干网络L-ELANnet, 采用L-ELANnet能实现检测精度无明显变化的前提下减少3/4的参数量; 提出基于ECA(Efficient Channel Attention)机制的空间金字塔池化(SPP)模块, 该模块能以更少的参数量实现不同尺度特征融合; 采用k-means++算法优化模型先验框, 提升模型对数据集中垃圾、起重机等细长目标的学习能力; 引入Wise-IoU作为边框损失函数, Wise-IoU通过为不同质量的数据提供动态非单调的梯度增益, 能从整体上提升模型的训练精度和收敛速度。最后开展消融和对比实验, 验证改进模型的有效性和优越性。实验结果表明, 与原始模型相比, 所提基于轻量YOLO k-means++算法的mAP@0.5从94.9%提高到95.4%, 检测速度提高了21.5%, 每秒帧数(FPS)达到113.6帧/s, 并且模型大小仅为14.9 MB, 参数数量是原模型的1/5。总体而言, 改进后的模型具有更高的检测精度和更快的检测速度, 同时与主流目标检测算法相比, 所提算法在输电铁塔状态检测方面具有更优越的性能。

关键词: 输电铁塔状态检测, CT-YOLO算法, L-ELANnet网络, k-means++算法, Wise-IoU损失函数

Abstract:

Transmission towers support power transmission lines and are critical to the operation of power systems. However, no dataset is currently available for detecting transmission tower status. To detect and warn about dangerous towers, this study first constructs an image dataset of tower status. The dataset comprises seven categories including foreign object intrusion, animal nests, base obstruction, and external damage. Next, the study proposes a lightweight algorithm, CT-YOLO, for detecting transmission tower status. This algorithm includes: 1)a lightweight backbone network, L-ELANnet, which reduces the parameter count by 3/4 while ensuring no significant change in detection accuracy; 2)a spatial pyramid pooling module based on the Efficient Channel Attention (ECA) mechanism, which achieves feature fusion at different scales with fewer parameters; 3)k-means++ to optimize the model's prior boxes, which improves the model's ability to learn about slender targets, such as debris and cranes, in the dataset; 4)Wise-IoU, a bounding box loss function, which provides dynamic non-monotonic gradient gains for data of different qualities, thereby improving training accuracy and convergence speed. Ablation and comparative experiments are conducted to verify the effectiveness and superiority of the improved model. Experimental results show that, compared to the original model, the proposed lightweight YOLO k-means++ algorithm increases mAP@0.5 from 94.9% to 95.4%, with a 21.5% increase in detection speed, reaching Frames Per Second (FPS) 113.6 frame/s. Furthermore, the model size is only 14.9 MB, which is 1/5 of the original model's. Overall, the improved model has higher detection accuracy and faster detection speed. Moreover, the proposed algorithm outperforms mainstream target detection algorithms in transmission tower condition detection.

Key words: transmission tower condition detection, CT-YOLO algorithm, L-ELANnet network, k-means++ algorithm, Wise-IoU loss function