Spiking Neural Network Based on Hierarchical Residual Quantization and Temporal Error Compensation for Remote Sensing Object Detection
Compared with traditional artificial neural networks (ANNs), spiking neural networks (SNNs) have lower computational complexity, lower energy consumption, and faster inference speed, making them more promising for practical deployment on edge devices. However, in SNNs, since the output spikes of neurons are discrete, the model may face information loss, especially when the membrane potential is quantized into binary spikes, where quantization errors can lead to model precision loss and information loss. To address these challenges, this study proposes a spiking neural network based on hierarchical residual quantization and temporal error compensation (HRQ-TEC-SNN) and used for remote sensing object detection tasks. Through hierarchical residual quantization and temporal error compensation design, higher resolution quantization of membrane potential can be performed and the quantization threshold of membrane potential can be dynamically adjusted to compensate for errors introduced during the quantization process, allowing for fine-tuning at each time step and reducing information loss caused by coarse quantization. In terms of network structure, by introducing depthwise separable convolution modules, channel attention and spatial attention mechanisms, and integrating fast spatial pyramid pooling based on pulse neural networks, the model’s detection accuracy has been further improved while reducing the number of model parameters and computational costs. Experimental results show that the HRQ-TEC-SNN achieves significant advantages in both accuracy and energy consumption on the DOTAv1.0, DOTAv1.5 and DIOR datasets.