用脉冲神经网络实现低功耗单目3D目标检测，性能超越传统方法。
SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding
- 提出跨尺度门控编码机制，增强脉冲网络特征表达能力。
- 在KITTI上达到11.78 (Easy)、10.69 (Moderate)、10.48 (Hard)的检测精度。
- 模型参数减少3倍，计算量降低10倍，适合边缘设备部署。
随着自动驾驶等领域对3D目标检测需求增加，能耗问题日益突出。脉冲神经网络（SNN）因低功耗特性成为潜在解决方案。本文将SNN应用于单目3D目标检测，提出SpikeSMOKE架构，首次探索低功耗单目3D检测新路径。针对SNN离散信号导致的信息损失问题，受生物神经元突触滤波机制启发，提出跨尺度门控编码机制（CSGC），通过注意力融合与门控滤波提升特征表达。同时设计轻量化残差块，在保持脉冲计算范式前提下降低计算量并加速训练。实验表明，该方法在KITTI、NuScenes-mini及CIFAR10/100数据集上均有效。相比基线SpikeSMOKE，引入CSGC后在KITTI数据集上AP|[email protected] IoU阈值下分别提升2.82（Easy）、3.2（Moderate）、3.17（Hard）。SpikeSMOKE-L（轻量版）相较SMOKE参数量减少3倍，计算量降低10倍，显著降低能耗。
原文摘要 · Abstract (English)
With the wide application of 3D object detection in some fields such as autonomous driving, its energy consumption is constantly increasing, making the research on low-power consumption alternatives a key research area. The spiking neural networks (SNNs), possessing low-power consumption characteristics, offer a novel solution for this research. Consequently, we apply SNNs to monocular 3D object detection and propose the SpikeSMOKE architecture, which represents a new attempt at low-power monocular 3D object detection. It's well known that the discrete signals of SNNs can lead to information loss compared to artificial neural networks (ANNs), which limits their feature representation capabilities. To solve this problem, inspired by the synaptic filtering mechanism of biological neurons, we propose a new Cross-Scale Gating Coding Mechanism (CSGC), which can enhance feature representation by combining cross-scale fusion of attentional methods and gated filtering mechanisms. In addition, to reduce the computation and accelerate training, we present a novel light-weight residual block that can maintain spiking computing paradigm and the highest possible detection performance. Our method is effective on the KITTI, NuScenes-mini and CIFAR10/100 datasets. Compared to the baseline SpikeSMOKE under the 3D Object Detection, the proposed SpikeSMOKE with CSGC can achieve 11.78 (+2.82, Easy), 10.69 (+3.2, Moderate), and 10.48 (+3.17, Hard) on the KITTI autonomous driving dataset by AP|R11 at 0.7 IoU threshold, respectively. It is worth noting that the results of SpikeSMOKE can significantly reduce energy consumption compared with the results of SMOKE. And SpikeSMOKE-L (lightweight) can further reduce the amount of parameters by 3 times and computation by 10 times compared to SMOKE.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。