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基于U-Net和跨特征图注意力机制的高压电线分割方法
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Abstract:
高压电线巡检对电网维护至关重要，它直接关系到电力系统的安全和人们的日常生活。随着机器学习和深度学习技术的不断发展，图像中高压电线的分割成为了智能巡检领域的研究热点。然而，由于背景复杂、对比度低以及高压电线在图像中占比小等因素，传统的高压电线分割方法往往难以达到理想的精度。为了解决这一问题，本研究在U-Net网络中引入了跨特征图注意力(Across-Feature Map Attention)机制，以增强网络对细小目标特征的学习能力。实验结果表明，在U-Net的第二层深度处加入该注意力机制后，与原始U-Net相比，准确率提高了6.35%，召回率提高了14.84%，F1分数提高了11.05%。
High-voltage power line inspection is crucial for grid maintenance, which is directly related to the safety of the power system and people’s daily lives. With the advancement of machine learning and deep learning technology, the segmentation of high-voltage power lines in images has become one of the research hotspots in intelligent inspection. However, due to factors such as complex background, low contrast, and the small proportion of high-voltage power lines in images, traditional high-voltage power line segmentation methods often make it difficult to achieve the desired accuracy. To solve this, this study introduces the Across-Feature Map Attention mechanism into the U-Net network to enhance the network’s ability to learn fine target features. The experimental results show that the U-Net with this attention mechanism added at the second layer depth outperforms the original U-Net, showing a 6.35% improvement in accuracy, a 14.84% increase in recall, and an 11.05% rise in the F1 score, respectively.
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