基于层级化注意力融合的航拍图像电力线分割模型

Power Line Segmentation Model of Aerial Images Based on Hierarchical Attention Fusion

电力线自动分割是保证智能检测平台安全运行的重要前提。然而,电力线分割是复杂背景、多种气候环境下的小目标分割问题,极易出现误检、漏检问题。为了提高电力线分割的鲁棒性与准确性,结合编码器-解码器框架,提出了一种基于层级化注意力融合的端到端分割模型。该模型提出一种降维残差卷积单元,增加网络深度的同时大幅度减少网络参数,更易部署于嵌入式设备。为了使模型捕捉到全局信息并强调电力线的目标区域,设计了链式层级化注意力融合模块进行多尺度特征融合,以解决类别不均衡问题。为了提高模型对电力线特有的直线先验特征的关注,提出了直线先验损失函数,并与Focal、Dice损失函数组合成联合损失函数,进一步提高了电力线分割准确度。实验结果表明,所提模型网络深度增加为基础网络的2.8倍左右,而参数量仅为原来的1/3左右。针对常规天气和雾天环境下的航拍图像均能实现电力线的鲁棒分割。所提出模型能应用于电力巡检领域,使巡检更加智能与高效。

Automatic power line segmentation is an important prerequisite for the safe operation of intelligent inspection platforms.However,power line segmentation is a small target segmentation problem in complex backgrounds and multiple climatic environments,which is highly prone to encounter false or missed detections.In order to improve the robustness and accuracy of power line segmentation,an end-to-end segmentation model based on hierarchical attention fusion is proposed in combination with an encoderdecoder framework.The model proposes a reduced-dimensional residual convolution unit that increases the network depth while significantly reducing the network parameters,making it easier to deploy in embedded devices,enabling the model to capture global information and emphasize the target regions of powerlines,a chain-based hierarchical attention fusion module is designed for multiscale feature fusion to address the category imbalance problem.To improve the model's attention to the unique line prior features of power lines,the line prior loss function is combined with the Focal loss function and Dice loss function to form a joint loss function to further improve the accuracy of power line segmentation.The experimental results show that the depth of the proposed model network increases to about 2.8 times that of the base network,while the number of parameters is only about 1/3 of the original one.Robust segmentation of power lines can be achieved for both regular weather and foggy weather aerial images.The proposed model can be applied to the field of power inspection,making the inspection more intelligent and efficient.