Abstract: To improve the segmentation accuracy and deployment efficiency of power line targets in power inspection images, a lightweight semantic segmentation network named LPL-Net (Light Powerline Net) was proposed. The network adopts a dual-branch feature extraction mechanism. The backbone path consists of a channel-adaptive Multi-scale Lightweight Convolution (MLC) module. This module constructs Lightweight Convolution (LightConv) modules using depthwise separable convolutions, combined with a channel attention mechanism Squeeze-and-Excitation Block (SEBlock), to efficiently extract local features while enhancing the adaptive weighting ability between channels. The auxiliary branch uses bilinear interpolation and shallow convolutions to extract multi-scale detail information, alleviating the feature loss of power lines caused by downsampling. Additionally, a (Multi-Direction-Aware Convolution (MDAC) module is introduced to improve the model's ability to model the elongated and directional structure of power lines. To validate the model's performance, a dataset named SPLS (Surveillance-based PowerLine Segmentation) was constructed, covering diverse and complex scenarios. Comparative experiments were conducted with representative lightweight semantic segmentation methods, including FR-Unet (Full-Resolution Network), CS^2-Net (Curvilinear Structure Segmentation Network), and MobileNet-V3. Experimental results show that LPL-Net, with only 0.48M parameters and 160.69 GFLOPs of floating-point operations, achieves a 0.22% and 1.12% improvement in F1-score and IoU (Intersection over Union), respectively, compared to the baseline model FRD-Net, with an approximate 2.8-fold increase in inference speed. This method balances accuracy and deployment friendliness, making it suitable for rapid power line target perception tasks in resource-constrained environments.

Key words: power line segmentation, lightweight network, depthwise separable convolution, attention mechanism, direction-aware convolution

摘要： 为了提升电力巡检图像中电力线目标的分割精度与模型部署效率，文中提出一种轻量的电力线语义分割网络(LPL-Net)。该网络采用双分支特征提取机制：主干路径由通道自适应多尺度轻量卷积模块(MLC)构成，结合深度可分离卷积构建的轻量卷积模块(LightConv)与通道注意力机制(SEBlock)，在实现局部特征高效提取的同时，还可增强通道之间的自适应加权能力；辅助路径通过双线性插值与浅层卷积提取多尺度细节信息，缓解下采样造成的电力线特征损失。同时，引入多方向感知卷积模块(MDAC)，提升模型对电力线细长且方向性结构的建模能力。为验证模型性能，本文构建了包含多种复杂场景的电力线图像分割的数据集(SPLS)，并与FR-Unet(Full-Resolution Network)、CS2-Net(Curvilinear Structure Segmentation Network)、MobileNet-V3等多种主流轻量级语义分割方法进行了对比实验。实验结果表明，在保持仅0.48 M 参数量和160.69 GFLOPs浮点运算量的情况下，LPL-Net 在 F1-score 与交并比(IoU)指标上分别较基线模型 FRD-Net 提升0.22%与1.12%，推理速度提高约2.8倍。该方法兼顾准确性与部署友好性，适用于资源受限环境下的电力线目标快速感知任务。

关键词: 电力线分割, 轻量级网络, 深度可分离卷积, 注意力机制, 方向感知卷积