Bringing RGB and IR Together: Hierarchical Multi-Modal Enhancement for Robust Transmission Line Detection
Shengdong Zhang, Xiaoqin Zhang, Wenqi Ren, Linlin Shen, Shaohua Wan,, Jun Zhang, Yujing M Jiang

TL;DR
This paper introduces HMMEN, a hierarchical multi-modal network that fuses RGB and IR images with alignment correction to improve transmission line detection in challenging conditions, aiding power infrastructure inspection.
Contribution
The paper proposes a novel HMMEN framework with mutual multi-modal enhancement and deformable convolution-based alignment, advancing multi-modal fusion for robust transmission line detection.
Findings
Outperforms state-of-the-art methods in diverse weather and lighting conditions
Reduces false positives and improves boundary delineation
Demonstrates robustness and practicality for UAV-based inspections
Abstract
Ensuring a stable power supply in rural areas relies heavily on effective inspection of power equipment, particularly transmission lines (TLs). However, detecting TLs from aerial imagery can be challenging when dealing with misalignments between visible light (RGB) and infrared (IR) images, as well as mismatched high- and low-level features in convolutional networks. To address these limitations, we propose a novel Hierarchical Multi-Modal Enhancement Network (HMMEN) that integrates RGB and IR data for robust and accurate TL detection. Our method introduces two key components: (1) a Mutual Multi-Modal Enhanced Block (MMEB), which fuses and enhances hierarchical RGB and IR feature maps in a coarse-to-fine manner, and (2) a Feature Alignment Block (FAB) that corrects misalignments between decoder outputs and IR feature maps by leveraging deformable convolutions. We employ MobileNet-based…
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Taxonomy
TopicsAdvanced Measurement and Detection Methods · Infrared Target Detection Methodologies · Thermography and Photoacoustic Techniques
