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<p>The examination of power transmission lines using UAVs (Unmanned Aerial Vehicles) is crucial for ensuring grid security. However, it is challenging for existing deep learning models to achieve a balance between accuracy and efficiency in recognizing power lines, especially when they are affected by intricate environmental backdrops and the thin structure of power lines. To address this issue, this paper proposes an improved model based on U-Net (PL-UNet), which aims to improve the ability of UAVs to recognize power lines in complex environments in real time. To reduce the model parameters, the lightweight EfficientNetV2-S is chosen as the encoder. To address the issues of information redundancy and insufficient local structure capture caused by the skip connections, we propose a multi-scale attention gate (MSAG) in the decoding part to improve the accuracy of key region feature extraction with less computational cost. Meanwhile, the dynamic weighted fusion (DWF) module is designed to effectively fuse the features through adaptive weighting to improve the flexibility of feature expression. After completing feature fusion, we further introduce a lightweight cross-stage partial pyramid block (CPPB) module, which performs multi-scale enhancement and channel optimization of the fused features through integrating multi-scale convolutional operations and separating and fusing feature channels. Finally, the hybrid loss function of weighted cross-entropy and dice is used to solve the category imbalance problem. Comparison experiments and ablation analysis are performed on top of the power line public dataset. The proposed PL-UNet has achieved 79.98% mIoU, with a parameter count of 21.27M and a detection speed of 56.86 fps. This shows that the network has a good real-time segmentation performance.</p>

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PL-UNet: a real-time power line segmentation model for aerial images based on adaptive fusion and cross-stage multi-scale analysis

  • 期刊论文

  • 发表日期：

    2025年1月7日

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  • Qian Zhao,
  • Haosheng Fang,
  • Yuye Pang,
  • Gehan Zhu,
  • Zhengzhe Qian

摘要

The examination of power transmission lines using UAVs (Unmanned Aerial Vehicles) is crucial for ensuring grid security. However, it is challenging for existing deep learning models to achieve a balance between accuracy and efficiency in recognizing power lines, especially when they are affected by intricate environmental backdrops and the thin structure of power lines. To address this issue, this paper proposes an improved model based on U-Net (PL-UNet), which aims to improve the ability of UAVs to recognize power lines in complex environments in real time. To reduce the model parameters, the lightweight EfficientNetV2-S is chosen as the encoder. To address the issues of information redundancy and insufficient local structure capture caused by the skip connections, we propose a multi-scale attention gate (MSAG) in the decoding part to improve the accuracy of key region feature extraction with less computational cost. Meanwhile, the dynamic weighted fusion (DWF) module is designed to effectively fuse the features through adaptive weighting to improve the flexibility of feature expression. After completing feature fusion, we further introduce a lightweight cross-stage partial pyramid block (CPPB) module, which performs multi-scale enhancement and channel optimization of the fused features through integrating multi-scale convolutional operations and separating and fusing feature channels. Finally, the hybrid loss function of weighted cross-entropy and dice is used to solve the category imbalance problem. Comparison experiments and ablation analysis are performed on top of the power line public dataset. The proposed PL-UNet has achieved 79.98% mIoU, with a parameter count of 21.27M and a detection speed of 56.86 fps. This shows that the network has a good real-time segmentation performance.