提出高效事件相机目标检测模型，显著降低计算开销。
Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection
- 引入稀疏卷积循环学习，实现92%以上激活稀疏性。
- 在100万像素和Gen1数据集上达成更高或相当的mAP，同时减少突触操作。
- 适合资源受限的边缘设备，尤其擅长长期时序学习任务。
利用事件相机的高时间分辨率和动态范围，事件相机上的目标检测可提升自动驾驶与机器人在真实场景中的性能与安全性。然而，处理稀疏事件数据需依赖计算密集的卷积循环单元，难以集成到资源受限的边缘设备中。本文提出稀疏事件高效检测器（SEED），用于神经形态处理器上的高效事件目标检测。通过稀疏卷积循环学习，在循环处理中实现超过92%的激活稀疏性，大幅降低对稀疏事件数据进行时空推理的计算成本。我们在Prophesee的1 Mpx和Gen1事件目标检测数据集上验证了该方法。结果表明，SEED在需要长期时序学习的任务中建立了新的计算效率基准。相比现有最优方法，SEED显著减少了突触操作次数，同时保持更高或相当的mAP。硬件仿真显示，SEED的硬件感知设计对实现低功耗、低延迟的神经形态处理至关重要。
原文摘要 · Abstract (English)
Leveraging the high temporal resolution and dynamic range, object detection with event cameras can enhance the performance and safety of automotive and robotics applications in real-world scenarios. However, processing sparse event data requires compute-intensive convolutional recurrent units, complicating their integration into resource-constrained edge applications. Here, we propose the Sparse Event-based Efficient Detector (SEED) for efficient event-based object detection on neuromorphic processors. We introduce sparse convolutional recurrent learning, which achieves over 92% activation sparsity in recurrent processing, vastly reducing the cost for spatiotemporal reasoning on sparse event data. We validated our method on Prophesee's 1 Mpx and Gen1 event-based object detection datasets. Notably, SEED sets a new benchmark in computational efficiency for event-based object detection which requires long-term temporal learning. Compared to state-of-the-art methods, SEED significantly reduces synaptic operations while delivering higher or same-level mAP. Our hardware simulations showcase the critical role of SEED's hardware-aware design in achieving energy-efficient and low-latency neuromorphic processing.
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