Abstract: Abstract: To address the issues of low efficiency, high misdetection rates and high omission rates in traditional power line inspections, an improved model based on YOLOv11n, named ETL-YOLO, was proposed. Firstly, to address the limited computational efficiency and insufficient multi-scale feature representation of conventional bottleneck designs, a lightweight multi-scale bottleneck structure, MSLW Bottleneck, was introduced. This structure was integrated into the C3k2 module to form the C3k2-MSLW feature extraction unit, enabling enhanced multi-scale representation while reducing both parameter count and computational overhead. Secondly, the loss of fine details caused by conventional downsampling convolutions was mitigated through the incorporation of the PEConv module, which leveraged the lightweight characteristics of PConv together with the multi-scale feature learning capability of EMA to enhance the detection of small objects. Finally, difficulties in identifying small targets under complex backgrounds were alleviated through the introduction of the PECAA attention mechanism, enabling selective enhancement or suppression of feature responses and thereby improving sensitivity to small objects in challenging environments. Experimental results on the public InsPLAD dataset show that the improved model achieves accuracy, recall, and mAP50 values of 93.5%, 92.6%, and 96.2%, with gains of 2.9%, 4.2%, and 1.7% over the baseline, respectively. The parameter count, computational complexity, and weight file size are reduced by 43.6%, 10.9%, and 40.3%. Overall, the model delivers a synergistic improvement in detection accuracy and efficiency and serves as an effective solution for UAV-based power line inspection.

Key words: Keywords: YOLOv11n, transmission line inspection, feature extraction, partial convolution, attention mechanisms, lightweight

摘要： 摘 要: 针对传统输电线路巡检效率低、误检率和漏检率高的问题，提出一种基于YOLOv11n改进的模型ETL-YOLO。首先，针对传统瓶颈模块计算效率低、多尺度特征提取能力弱的问题，提出具有多尺度特征提取能力的轻量化瓶颈结构MSLW Bottleneck，并将MSLW Bottleneck融入到C3k2模块中，提出C3k2-MSLW特征提取模块，用以提升模型的多尺度特征提取能力并降低模型的参数量和计算量。其次，针对传统下采样卷积容易丢失小目标细节信息的问题，提出PEConv模块，利用PConv的轻量化特性和EMA的多尺度特征学习能力，提升模型对小目标的检测能力。最后，针对复杂背景小目标难以有效检测的问题，提出PECAA注意力机制，使模型能够对不同特征进行选择性加强或抑制，以增强模型在复杂环境下对小目标的感知能力。在公开数据集InsPLAD上的实验结果表明，改进后的模型准确率、召回率、mAP50分别达到了93.5%、92.6%、96.2%，较基线模型分别提升了2.9%、4.2%和1.7%，参数量、计算量和权重文件大小分别下降43.6%、10.9%、40.3%，实现了检测精度和检测效率的协同提升，为无人机输电线路巡检提供了高效的技术方案。

关键词: 关键词: YOLOv11n, 输电线路巡检, 特征提取, 部分卷积, 注意力机制, 轻量化