TWL Spike Yolo

TWL Spike Yolo is a spiking neural network (SNN) for real-time object detection based on event-based vision. The model adapts the YOLOv8 architecture to work with streams of event data, allowing efficient processing in neuromorphic computing environments.

Demo GIF
Demonstration of model performance on the Gen1 dataset

This approach leverages the low-latency and power-efficient properties of SNNs to detect objects in fast-changing visual scenes. The model also explores multimodal fusion by combining event-based and frame-based inputs to enhance detection accuracy under challenging conditions such as motion blur or low light.

Highlights

Architecture: YOLOv8-inspired spiking neural network.
Input: Event data from neuromorphic (event-based) cameras; optionally combined with standard image frames.
Use case: High-speed, low-latency object detection with improved energy efficiency.
Applications: Robotics, autonomous driving, surveillance, and edge devices using neuromorphic hardware.

Source Code

The implementation, training scripts, and inference tools are available in the GitHub repository:
👉 https://github.com/KirillHit/twl_spike_yolo