VisionFault-350K: A Large-Scale Fault Injection Dataset for Robotic Vision Systems

Dataset Description: 

A large-scale fault-augmented dataset contains 350,751 images derived from real robotic camera recordings. To simulate real-world edge cases, original frames were transformed via Stable Diffusion (img2img) across a wide range of fault scenarios.

Technical Specifications

• Total Images: 350751

• Primary Tasks: Lane Following and Obstacle Detection.

• LLM Engine: Fault scenarios designed using GPT-OSS 120B.

• Synthesis Engine: Visual Faults synthesized using Stable Diffusion 2.1 Base.

•Categories: Features diverse fault types, such as Camera Failures, Motion Blur, Extreme Weather (Ice, Rain, Fog), Low Light (Tunnel, Night, Backlight), Lens Distortions, etc.

Usage

 If "Download All" is not working, please download the files one by one. To merge the parts after downloading:

For Linux/Mac:

cat part* > dataset.zip 

For Windows (PowerShell - Recommended):

Get-Content part* -ReadCount 0 -Encoding Byte | Set-Content dataset.zip -Encoding Byte

For Windows (CMD):

copy /b part* dataset.zip

if not working : cmd /c copy /b part* dataset.zip

License & Disclaimer

• License: Creative Commons Attribution 4.0 International (CC BY 4.0).

Disclaimer:

Dataset Integrity: This dataset is generated via LLM+LDM and provided "as-is" for robotic vision system testing and research.

Version 2.0 Enhancement: Version 2 features a refined image corpus with optimized visual quality.