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Multi-Strategy Lightweight Insulator Defect Detection for Jetson Orin NX Real-time Onboard Application
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DOI：10.1142/S0218001426590068.png)
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Abstract
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To address challenges in small target detection, complex backgrounds, and lightweight deployment for high-voltage line insulators, this paper proposes a Mobile Lightweight Insulator Defect Detection (MLIDD) system with a novel SPM-YOLO algorithm. Key innovations include: (1) an SPD-Conv module replacing traditional downsampling to preserve fine-grained details of small targets; (2) a PPA attention module enhancing spatial perception via multi-branch feature extraction and adaptive fusion; (3) integration of the MobileNet V4 backbone to reduce computational complexity. Experiments show SOM-YOLO improves detection accuracy and robustness while enabling efficient model compression. The system was successfully deployed on the Jetson Orin NX platform, achieving real-time, high-precision defect detection in UAV inspections.
Keywords:
Deep learning
target detection
attention mechanism
lightweighting
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