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Cross-modal collaborative optimization for semantic segmentation of RGB-T power equipment images.

Huan Chen, Yan Zhou, Qingwu Li

|March 17, 2026

Summary

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This study introduces CCONet, a novel network for RGB-T semantic segmentation of power equipment. CCONet enhances feature representation using cross-modal collaborative optimization for improved substation monitoring.

Area of Science:

Computer Vision
Artificial Intelligence
Electrical Engineering

Background:

Semantic segmentation is crucial for interpreting complex substation environments.
RGB and thermal infrared (RGB-T) data offer complementary information for enhanced feature representation.
Existing fusion strategies often overlook modality-specific advantages at different semantic levels.

Purpose of the Study:

To propose CCONet, a cross-modal collaborative optimization network for RGB-T semantic segmentation of power equipment.
To address limitations of single-stage fusion by leveraging hierarchical and task-specific fusion strategies.

Main Methods:

CCONet utilizes a three-stage framework: feature extraction, multimodal fusion, and decoding.
Integrates feature pyramid and path aggregation networks for hierarchical information flow.

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Employs three task-specific fusion modules: feature excitation (high-level), feature localization (mid-level), and feature refinement (low-level).

Main Results:

CCONet achieves competitive performance on a custom RGB-T substation dataset and public benchmarks.
Demonstrates superior semantic segmentation accuracy for power equipment compared to state-of-the-art methods.
Highlights the effectiveness of joint cross-modal interactions and task-specific fusion.

Conclusions:

CCONet offers a robust approach to multimodal image analysis for power system monitoring and inspection.
The cross-modal collaborative optimization strategy is generalizable to other complex scenarios with retraining.
This work advances semantic segmentation techniques in challenging industrial environments.