Support Structure Detection of Railway Catenary Systems on UAVs Using Spike-Based Brain-Inspired Neural Network

The Catenary Support Component (CSC) is a critical element in ensuring the safe operation of electrified railway systems. In recent years, the use of UAVs for railway maintenance and inspection has steadily increased, making them an increasingly important technological tool. However, UAVs face several challenges during railway inspections. These challenges include energy consumption constraints, which limit their prolonged use, as well as complex backgrounds, structural occlusions, and varying component scales that hinder detection accuracy. To address these challenges, a low-energy consumption, graph-guided spike-based brain-inspired neural network algorithm (GSINet) was proposed. First, a novel backbone called the spatiotemporal multiscale spike fusion network was designed. This network leverages spatiotemporal spike encoding for feature modeling and enables cross-scale feature fusion. Temporal and channel attention mechanisms, along with SpikeASPP technology, were used to enhance the network’s ability to handle diverse scales and complex environmental conditions. Next, a Spike-inspired detection head was designed with a position guidance module (PGM) based on graph convolutional networks. The PGM utilizes prior category relationships, integrating both semantic and geometric information to mitigate challenges such as occlusion and multi-component measurement. Finally, a spiking network architecture was implemented using the improved Leaky Integrate-and-Fire (IB-LIF) Neuron. This reduces energy consumption by exploiting sparse computations and an event-driven mechanism, while ensuring high detection accuracy. Extensive experiments on a CSC dataset captured by UAVs demonstrate that the model achieves superior measurement performance while consuming significantly lower energy.