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Updated: Jan 13, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Precise segmentation method for slender power targets based on multi-scale perception and location-sensitive

Dong Zhang¹, Pengjun Xie², Haowei Chen³

¹State Grid Huaian Power Supply Company, State Grid Jiangsu Electric Power Co. Ltd., Huaian, 223002, Jiangsu, China. 1146310390@qq.com.

Scientific Reports

|January 9, 2026

Summary

This summary is machine-generated.

This study introduces MSHNet, a new deep learning model for precise wire segmentation in power systems. It improves safety and maintenance by accurately identifying wires even with background noise and scale variations.

Keywords:

Distribution power lines Live line work MSHNet Semantic segmentation

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Area of Science:

Electrical Engineering
Computer Vision
Artificial Intelligence

Background:

Precise wire segmentation is crucial for power system safety and maintenance.
Existing methods struggle with background interference and scale variations in complex environments.

Purpose of the Study:

To propose MSHNet, a novel architecture for high-precision and efficient intelligent wire segmentation.
To overcome limitations in geometric feature perception and spatial contextual modeling of conventional networks.

Main Methods:

Developed MSHNet, integrating multi-scale head networks with scale- and location-sensitive loss functions.
Utilized multi-scale feature analysis and location-aware learning theories.
Constructed scale-aware head modules and position-sensitive loss functions for unified spatial information decoding and pixel-level optimization.

Main Results:

Achieved refined wire segmentation and semantic understanding in complex scenarios.
Demonstrated precise wire extraction and semantic segmentation detection in power transmission/distribution scenarios using an intelligent experimental platform.

Conclusions:

MSHNet offers innovative technical solutions for intelligent facility maintenance in complex environments.
This research expands the theoretical framework of electrical vision inspection.