PEOD Dataset

Pixel-aligned Event-RGB Object Detection under Challenging Scenarios

The first large-scale multimodal benchmark providing synchronized high-resolution event streams and RGB images for object detection under extreme conditions

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Dataset Overview

PEOD addresses critical limitations in existing Event-RGB datasets: sparse coverage of extreme conditions and low spatial resolution (≤640×480)

57%

of data captured under challenging conditions (low-light, overexposure, high-speed motion)

120+ Synchronized Sequences
340K Verified Bounding Boxes
72K High-Quality Data Pairs
1280×720 High Resolution
>87dB High Dynamic Range
30Hz Annotation Frequency

Coaxial Dual-Camera System

True pixel-level spatial alignment and microsecond-level temporal synchronization using beam-splitter optical system

Challenging Environments

Urban, suburban, and tunnel environments with 60% data under extreme conditions (low-light, overexposed, high-velocity)

Six Object Classes

Meticulously verified annotations for car, bus, truck, two-wheeler, three-wheeler, and person

Microsecond Precision

Hardware signal generator ensures microsecond-level synchronization between RGB and event cameras

Split Sequences Frames Bounding Boxes Conditions
Training ~85 ~57,000 270k Diverse illumination & motion conditions
Test ~35 ~15,000 70k Held-out sequences for benchmarking
Total 120+ 72k 340k Complete dataset coverage

Challenging Scenarios Showcase

Representative examples demonstrating the limitations of conventional cameras and the advantages of event-based sensing

PEOD dataset challenging scenarios showcase
Challenging Scenarios from PEOD Dataset These examples demonstrate sensor failure scenarios where conventional frame-based cameras struggle: (Top and third rows) Overexposure in high dynamic range scenes leading to complete information loss; (Second row) Motion blur from high-speed movement; (Bottom row) Extreme low-light conditions with severe underexposure. In contrast to the degraded RGB images, the event-based streams (left side of each pair) consistently preserve crucial structural details and object boundaries, enabling robust detection even under these extreme conditions. This showcases the complementary nature of the two modalities and the critical importance of multimodal fusion for all-weather perception systems.

Overexposure Resilience

Event cameras maintain object visibility when RGB sensors saturate under bright lighting conditions

Motion Blur Immunity

High temporal resolution eliminates motion blur artifacts that degrade conventional camera performance

Low-Light Excellence

Superior performance in extreme low-light where RGB cameras fail to capture meaningful information

Dual-Camera Acquisition System

Our custom coaxial optical system ensures pixel-perfect alignment and precise synchronization

Coaxial dual-camera system setup
Coaxial Imaging System Our acquisition system utilizes a JCOPTIX OSB25R55-T5 non-polarizing plate beam splitter (50:50 split ratio) and MCC1-1S 10mm coaxial cube. The system comprises a Prophesee EVK4 HD event camera (1280×720) and Hikvision MV-CS050-10UC industrial RGB camera (2448×2048, 60fps), both equipped with identical Hikvision 25mm C-mount fixed-focal-length lenses for consistent imaging characteristics.

Hardware Synchronization

Single square-wave signal generator provides hardware trigger pulses to both cameras for microsecond-level accuracy

Pixel-Level Alignment

Shared optical path enables true pixel-level spatial alignment through standard stereo rectification

Identical Optics

Both cameras use identical lenses and fixed aperture settings to eliminate focal length and distortion discrepancies

Dataset Statistics & Visualizations

Comprehensive analysis of data distribution and sample annotations from challenging scenarios

Dataset statistics and sample visualizations
Dataset Statistics and Challenging Scenarios The figure shows the temporal distribution of our dataset with 57.1% captured under challenging illumination conditions, alongside sample aligned Event-RGB pairs from diverse driving scenarios. Our data includes urban roads, suburban areas, complex intersections, underpasses, tunnels, and highways captured continuously from 04:00 to 24:00, covering the full spectrum of lighting conditions from dawn to nighttime.

Illumination Analysis

Quantitative classification using under-exposure (S_LL) and over-exposure (S_OE) scores based on pixel saturation

Low-Light Conditions

Extensive coverage of challenging scenarios where conventional cameras fail but event cameras excel

Overexposure Scenarios

High dynamic range scenes demonstrating the >87dB HDR advantage of event cameras over RGB sensors

Dataset Download

The PEOD dataset is now officially released and available for download.

Baidu Netdisk

Download the full PEOD dataset

Extraction Code: g97d

Multiple Formats

Event data in RAW and DAT formats, annotations in NumPy format

Easy Access

Hosted on Baidu Netdisk

Code & Tools

Evaluation scripts and baseline implementations included

Citation

If you use PEOD in your research, please cite our arXiv paper below (accepted at AAAI 2026).

Status: Accepted at AAAI 2026

@article{cui2025peod,
  title={PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions},
  author={Cui, Luoping and Liu, Hanqing and Liu, Mingjie and Lin, Endian and Jiang, Donghong and Wang, Yuhao and Zhu, Chuang},
  journal={arXiv preprint arXiv:2511.08140},
  year={2025}
}