基于深层特征高效提取的脉冲神经网络目标检测方法

Spiking Neural Network Target Detection Method Based on Efficient Deep Feature Extraction

摘要: 由于出色类脑机制和自身节能性，脉冲神经网络(SNN)受到广泛研究，且在目标分类任务中取得显著进展，但其在目标检测领域的研究尚处于初步阶段。针对现有SNN目标检测算法在复杂场景下检测精度低的问题，本文构建了一种深层特征高效、轻量提取的脉冲神经网络目标检测架构——ES-YOLO。该架构提出了高效特征提取模块(SDF-Module)，并结合多尺度特征提取的空间金字塔设计，显著提升了模型在复杂目标检测任务中的性能。此外，通过引入脉冲解耦检测头，进一步优化了模型的检测精度和实时性。实验结果表明，在VOC2012数据集上，ES-YOLO模型获得了60.5%的mAP@0.5和37.1%的mAP@0.5:0.95的性能指标，相比EMS-YOLO分别提升了4%和3.7%。该模型不仅缩小了与同等架构ANN模型的性能差距，而且整体能耗为同等架构ANN的1/5。为后续SNN在目标检测任务中的广泛应用提供支持。

Abstract: Due to the outstanding brain-inspired mechanisms and energy efficiency, Spiking Neural Networks (SNN) have garnered widespread attention and achieved significant progress in object classification tasks. However, research on SNNs in the field of object detection remains in its early stages. To address the issue of low detection accuracy of existing SNN-based object detection algorithms in complex scenarios, this paper proposes a novel, deep-feature-efficient and lightweight SNN-based object detection architecture—ES-YOLO. The architecture introduces an efficient feature extraction module (SDF-Module) and incorporates a spatial pyramid design for multi-scale feature extraction, significantly improving the model’s performance in complex object detection tasks. Furthermore, by integrating a spiking decoupled detection head, the model’s accuracy and real-time performance are further optimized. Experimental results on the VOC2012 dataset demonstrate that the ES-YOLO model achieves 60.5% mAP@0.5 and 37.1% mAP@0.5:0.95, representing improvements of 4% and 3.7% respectively compared to EMS-YOLO. The model not only reduces the performance gap between SNNs and equivalent ANN models but also achieves overall energy consumption that is only 1/5 that of the equivalent ANN architecture. This work provides support for the broader application of SNNs in object detection tasks in the future.