UAV-Based Edge Inference for Insulator Defect Detection on Transmission Lines via Convolutional Feature Reuse

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Abstract
Unmanned aerial vehicles (UAVs) equipped with onboard processors enable edge intelligence for smart-grid Internet of Things (IoT) inspection, yet insulator defect detection from aerial imagery remains challenging due to cluttered backgrounds, small targets, and strict latency constraints. Compared with handcrafted methods that rely on manual feature design and parameter tuning, this article presents an end-to-end improved Faster R-CNN detector whose feature extractor is redesigned via convolutional feature reuse (CFR) and a two-layer multiscale fusion scheme with a deeper extraction path, preserving fine-grained cues while limiting model complexity. Experiments on Chinese Power Line Insulator dataset (CPLID) and a newly constructed Glass Insulator dataset (GID) achieve mAP@0.5 values of 97.75% and 96.79%, respectively, which is 7.1% higher than a standard Faster R-CNN (VGG16) baseline on CPLID while reducing the model size to 58 MB. On a UAV system (DJI M300 + H20T + Jetson Xavier NX), TensorRT acceleration runs at 8.5 frames/s, outperforming Faster R-CNN (3.2 frames/s) and YOLOv11 (5.3 frames/s) under the same edge setting. Outdoor flight tests on 330-kV transmission lines (TLs) further validate field readiness, supporting low-latency and bandwidth-efficient UAV inspection for smart-grid IoT maintenance.
Funding Information
  • National Natural Science Foundation of China (61833013)
  • National Natural Science Foundation of China (61903297)
  • National Natural Science Foundation of China (62103326)
  • Young Talent fund of University Association for Science and Technology in Shaanxi (20210114)
  • Aeronautical Science Foundation of China (2024Z0340T6001)
  • Natural Sciences and Engineering Research Council of Canada (2024Z0340T6001)

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