Wang Yan, Yuan Qinghe, Wang Ying, Ruizhi Zhang, Wu Qian, Feng Guoliang
Economic and Technological Research Institute, State Grid Heilongjiang Electric Power Co. Ltd., Harbin, Heilongjiang, China.
College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, Jilin, China.
PeerJ Comput Sci. 2024 Oct 10;10:e2383. doi: 10.7717/peerj-cs.2383. eCollection 2024.
The identification of foreign objects on transmission lines is crucial for their normal operation. There are risks and difficulties associated with identifying foreign objects on transmission lines due to their scattered distribution and elevated height.
The dataset for this paper consists of search material from the web, including bird nests, kites, balloons, and rubbish, which are common foreign objects found on top of transmission lines, totaling 400 instances. To enhance the classical U-Net architecture, the coding component has been substituted with a ResNet50 network serving as the feature extraction module. In the decoding section, a batch normalization (BN) layer was added after each convolutional layer in the decoder to improve the model's efficiency and generalization capacity. Additionally, a combined loss function was implemented, merging Focal loss and Dice loss, to tackle class imbalance issues and improve accuracy.
In summary, RB-UNet, a novel semantic segmentation network, has been introduced. The experimental results show a mIoU of 88.43%, highlighting the significant superiority of the RB-UNet approach compared to other semantic segmentation techniques for detecting foreign objects on transmission lines. The findings indicate that the proposed RB-UNet algorithm is proficient in detecting and segmenting foreign objects on transmission lines.
识别输电线路上的异物对其正常运行至关重要。由于输电线路上的异物分布分散且位置较高，识别异物存在风险和困难。
本文的数据集由从网络搜索的材料组成，包括鸟巢、风筝、气球和垃圾，这些是在输电线路顶部发现的常见异物，共计400个实例。为了改进经典的U-Net架构，编码组件已被替换为作为特征提取模块的ResNet50网络。在解码部分，在解码器的每个卷积层之后添加了批归一化（BN）层，以提高模型的效率和泛化能力。此外，实现了一种组合损失函数，将焦点损失和骰子损失合并，以解决类别不平衡问题并提高准确性。
总之，引入了一种新颖的语义分割网络RB-UNet。实验结果表明平均交并比为88.43%，突出了RB-UNet方法在检测输电线路上的异物方面相对于其他语义分割技术的显著优势。研究结果表明，所提出的RB-UNet算法能够熟练地检测和分割输电线路上的异物。
