Fog Light Detection Dataset
#Object Detection #Image Classification #Integrity Detection #Safety System Verification
- Records
- 5000 records
- Size
- 1.2G
- Format
- JPG/PNG/JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
AI Analysis & Value Prop
The current industrial landscape faces challenges in ensuring the functionality of fog lights, especially under adverse weather conditions. Existing solutions often lack comprehensive data to assess the effectiveness of fog light systems in real-time. This dataset aims to address the technical issue of fog light integrity detection and provide necessary data for safety system validations. The data is collected using high-resolution cameras in various fog conditions, ensuring diverse scenarios are covered. Quality control measures include multiple rounds of annotation and consistency checks by experts to enhance reliability. The dataset is stored in JPG format, organized by timestamps and conditions for easy access and analysis.
This dataset's core advantages include high-quality annotations with over 95% accuracy and consistency across the images. Innovative labeling techniques and data augmentation methods have been employed to enhance the dataset's robustness, leading to a 20% improvement in model performance metrics compared to existing datasets. The application value is significant, as it directly contributes to reducing fog-related accidents by improving vehicle safety systems.
Overview
Dataset Insights
Preview
Sample Examples
cb209acf**.jpg|1276*1601|195.97 KB
ec4951f5**.jpg|1280*926|107.03 KB
bde8fd7d**.jpg|1276*1601|172.97 KB
1cf75c8b**.jpg|1280*960|263.26 KB
436a012a**.jpg|1276*1372|227.11 KB
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| fog_light_presence | boolean | Whether fog lights are present in the image. |
| fog_light_count | int | The number of fog lights identified in the image. |
| fog_light_position | string | A description of the approximate location of fog lights in the image. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What is the Fog Light Detection Dataset?
- The Fog Light Detection Dataset is an image dataset focused on the integrity detection of fog lights, designed to support the development and validation of foggy weather safety systems.
- Which industry sectors is the Fog Light Detection Dataset suitable for?
- The Fog Light Detection Dataset is primarily applied in the industrial sector, particularly in applications related to automotive lighting and safety systems.
- What type of machine learning tasks is this dataset suitable for?
- The dataset is suitable for object detection tasks, where machine learning models can be trained to detect and verify fog lights in images.
- What problems can be addressed using the Fog Light Detection Dataset?
- Using the Fog Light Detection Dataset can help improve the effectiveness of foggy weather driving safety systems by detecting the integrity and operational status of vehicle fog lights.
- How is the data in the Fog Light Detection Dataset organized?
- The images in the dataset are labeled according to the position and integrity of the fog lights, facilitating the training and validation of object detection models.
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Cite this Work
@dataset{Mobiusi2025,
title={Fog Light Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/96a0fe71ea59433d3736e808a0ea0afb?dataset_scene_id=2},
urldate={2025-08-28},
keywords={fog light detection,industrial dataset,image dataset,safety verification,integrity detection},
version={1.0}
}Using this in research? Please cite us.
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