Power line extraction is useful for low aircraft altitude obstacle avoidance, and it also plays an important role in unmanned aerial vehicle (UAV)-based power autonomous inspection. However, the current power line detection algorithms have the disadvantages of slow detection speed and poor detection performance because the scale of objects varies greatly in UAV images. We proposed an efficient power line extraction method named power line extraction network (PLENet) to achieve high accuracy (ACC) and speed of power line detection in complex scenes. The proposed method overcomes the imbalance of the positive and negative samples, detects the power lines in images with different scales, and achieves good ACC and speed performance at the same time. For this method, we introduced convolution kernels of various sizes in the input layer and concatenated the extracted features to obtain more robust feature maps. Then, we implemented the class-balanced cross-entropy loss function to overcome the problem of the imbalance between positive and negative samples. Finally, we proposed a bilinear interpolation upsampling module in the output layer to replace the deconvolution to solve the gridding effect. To evaluate the effectiveness of the proposed method, we conducted a series of experiments on our collected aerial imagery dataset with complex scenes. The ACC and intersection over union of the proposed method are up to 86.22% and 71.7% on the server, respectively, whereas 86.61% and 70.58% on the mobile terminals, respectively. The experimental results show that our PLENet has apparent advantages in multiple indicators compared with other networks. The multiscene experiment results also demonstrate that PLENet has strong robustness, overcoming the influence of complex scenes and accurately extracting power lines.

中文翻译：

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PLENet：基于无人机航拍图像的高效电力线提取网络

电力线提取对于低空飞行器避障非常有用，在基于无人机（UAV）的电力自主巡检中也发挥着重要作用。然而，目前的电力线检测算法存在检测速度慢、检测性能差的缺点，因为无人机图像中物体的尺度变化很大。我们提出了一种名为电力线提取网络（PLENet）的高效电力线提取方法，以实现复杂场景中电力线检测的高精度（ACC）和速度。该方法克服了正负样本的不平衡性，检测不同尺度图像中的电力线，同时取得了良好的ACC和速度性能。对于这种方法，我们在输入层引入了各种大小的卷积核，并将提取的特征连接起来以获得更鲁棒的特征图。然后，我们实现了类平衡交叉熵损失函数来克服正负样本不平衡的问题。最后，我们在输出层提出了一个双线性插值上采样模块来代替反卷积来解决网格化效果。为了评估所提出方法的有效性，我们对我们收集的具有复杂场景的航空影像数据集进行了一系列实验。该方法的ACC和交集在服务器上分别达到86.22%和71.7%，而在移动端分别达到86.61%和70.58%。实验结果表明，与其他网络相比，我们的 PLENet 在多个指标上具有明显优势。多场景实验结果也证明了PLENet具有很强的鲁棒性，克服了复杂场景的影响，准确地提取了电力线。