针对输电线路故障检测中存在的小目标、漏检和图像分辨率低等问题，提出了一种基于多尺度特征融合的输电线路故障检测算法。为了获得不同尺度的输入特征，首先将图像输入给预先训练的共享残差网络。然后，利用特征融合注意力机制来学习不同尺度下的显著特征，整合大尺度特征图上的详细信息和小尺度特征图上的场景信息。此外，受语义分割中密集空间金字塔池的启发，构建多尺度特征融合密集金字塔，进一步提高特征提取的能力。最后，利用尺度不变误差损失来预测对数空间中的深度映射。在输电线路故障检测数据集上的实验数据表明，所提方法具有较高的检测准确度，平均精度均值（mean average precision，mAP）评分达到97.50 %，检测速度可达56帧/s，具有较高的鲁棒性和准确性，为输电线路故障的智能监测提供有效的解决方案。

To address the challenges of small targets, missed detections, and low image resolution in transmission line fault detection, a multi-scale feature fusion-based algorithm for transmission line fault detection is proposed. To obtain input features at different scales, images are first fed into a pre-trained shared residual network. Then, a feature fusion attention mechanism is used to learn salient features at different scales, integrating detailed information from large-scale feature maps and contextual information from small-scale feature maps. Additionally, inspired by dense spatial pyramid pooling in semantic segmentation, a multi-scale feature fusion dense pyramid is constructed to further enhance feature extraction capabilities. Finally, a scale-invariant error loss is utilized to predict depth mapping in logarithmic space. Experimental results on a transmission line fault detection dataset demonstrate that the proposed method achieves the highest detection accuracy, with a mean average precision (mAP) score of 97.50% and a detection speed of up to 56 frames per second. It exhibits high robustness and accuracy, providing an effective solution for intelligent monitoring of transmission line faults.