Cross-Modal Alignment and Fusion for RGB-D Transmission-Line Defect Detection

• URL: http://arxiv.org/abs/2602.01696v2
• Date: Tue, 03 Feb 2026 07:57:04 GMT
• Title: Cross-Modal Alignment and Fusion for RGB-D Transmission-Line Defect Detection
• Authors: Jiaming Cui, Wenqiang Li, Shuai Zhou, Ruifeng Qin, Feng Shen,
• Abstract summary: This paper proposes CMAFNet, a Cross-Modal Alignment and Fusion Network that integrates RGB appearance and depth geometry through a principled-then-fuse paradigm.<n> CMAFNet consists of a Semantic Recomposition Module that performs dictionary-based feature purification.<n>A lightweight variant reaches 24.8% mAP50 at 228 FPS with only 4.9M parameters, surpassing all YOLO-based detectors.
• Score: 11.637942429146172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transmission line defect detection remains challenging for automated UAV inspection due to the dominance of small-scale defects, complex backgrounds, and illumination variations. Existing RGB-based detectors, despite recent progress, struggle to distinguish geometrically subtle defects from visually similar background structures under limited chromatic contrast. This paper proposes CMAFNet, a Cross-Modal Alignment and Fusion Network that integrates RGB appearance and depth geometry through a principled purify-then-fuse paradigm. CMAFNet consists of a Semantic Recomposition Module that performs dictionary-based feature purification via a learned codebook to suppress modality-specific noise while preserving defect-discriminative information, and a Contextual Semantic Integration Framework that captures global spatial dependencies using partial-channel attention to enhance structural semantic reasoning. Position-wise normalization within the purification stage enforces explicit reconstruction-driven cross-modal alignment, ensuring statistical compatibility between heterogeneous features prior to fusion. Extensive experiments on the TLRGBD benchmark, where 94.5% of instances are small objects, demonstrate that CMAFNet achieves 32.2% mAP@50 and 12.5% APs, outperforming the strongest baseline by 9.8 and 4.0 percentage points, respectively. A lightweight variant reaches 24.8% mAP50 at 228 FPS with only 4.9M parameters, surpassing all YOLO-based detectors while matching transformer-based methods at substantially lower computational cost.

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