A spiking neural network for energy-efficient power transmission line inspection

Foreign object detection is a critical task in power transmission line inspection, where large-scale power transmission infrastructure networks must be monitored reliably using UAV-based or mobile visual systems operating under strict energy constraints. Existing deep learning–based detectors achieve high accuracy but incur substantial computational and power costs, limiting their suitability for long-duration or large-area inspection. Spiking neural networks (SNNs) offer an energy-efficient alternative; however, their application to complex object detection tasks has been constrained by architectural limitations that prevent the incorporation of advanced feature fusion modules commonly used in modern detectors. To address this limitation, SpikeC3-YOLO is proposed. The framework introduces the SpikeC3 module, which supports C3-level feature fusion while preserving full spike compatibility. A unified pre-convolution spiking strategy is employed to preserve event-driven computation while supporting complex feature interactions. Comprehensive experiments on a real transmission line inspection dataset show that the proposed method achieves 57.30% [email protected]:0.95, comparable to the ANN-based YOLOv8 (55.80%), while reducing theoretical energy consumption by 48.3% relative to the baseline Spike-YOLO. Furthermore, an ultra-efficient variant maintains competitive detection performance while consuming only 13.8% of the baseline energy. These results demonstrate that sophisticated detection architectures can be realised within spiking neural networks without sacrificing energy efficiency, enabling practical, energy-aware visual inspection solutions for large-scale power transmission lines. Copyright © 2026 The Author(s).