mlsanityData to 14 Oct 2026

Sequence-SOD: Bio-inspired Sequence-aware Spiking ObjectDetection for Event Cameras

arXiv 2607.26703 ↗First posted 29 Jul 2026cs.CVcited by 0

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Abstract (arXiv)

Event cameras follow a retina-inspired sensing principle, reporting local intensity changes asynchronously with hightemporal resolution and a wide dynamic range. Spiking Neural Networks (SNNs) complement these sparse event streams through brain-inspired dynamics, using sparse spikes and leaky membrane potentials to integrate information over time. However, many SNN object detectors process isolated event intervals with a single label and reset the network state after each prediction, thereby underusing temporal information in continuous event streams. We introduce Sequence-SOD, a sequence-aware SNN object detector that processes extended event sequences containing labels at multiple time points. Events are accumulated into short intervals, discretized into temporal steps, and fed sequentially to an SSD-style Spiking DenseNet while preserving membrane potentials across intervals within a sequence, so that detection is driven by an evolving neural state instead of independently reset input windows. On the Gen1 Automotive Detection Dataset, sequence-aware training improves mAP from 23.38 for single-interval training to 25.30 without augmentation and to 26.88 withevent-data augmentation. The model achieves a theoretical prediction frequency of 40 Hz. Training and evaluating SNN object detectors on extended event sequences improves their ability to exploit temporal cues while preserving the energy-efficiency benefits of sparse spiking computation. The results highlight sequence-aware training as a complementary direction to architectural improvements for event-based SNN detection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 29 Jul 2026.

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References · 28

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16 more
  1. 2020A Large Scale Event-based Detection Dataset for Automotivecited by 109
  2. 2022Object Detection with Spiking Neural Networks on Automotive Event Datacited by 80
  3. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 77
  4. 2024State Space Models for Event Camerascited by 75
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  6. 2024Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detectioncited by 67
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  11. 2024SFOD: Spiking Fusion Object Detectorcited by 20
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  13. 2024EvRT-DETR: Latent Space Adaptation of Image Detectors for Event-based Visioncited by 9
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  15. 2025Hybrid Spiking Vision Transformer for Object Detection with Event Camerascited by 7
  16. 2023Automotive Object Detection via Learning Sparse Events by Spiking Neuronscited by 5

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