Turn Signal Detection Dataset
#Object Detection #Image Classification #Vehicle Detection #Indicator Module Recognition
- 15000 records
- 3.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-08-15
AI Analysis & Value Prop
The Turn Signal Detection Dataset is designed to address the challenges in vehicle turn signal system matching detection and indicator light module recognition. Currently, the automotive industry faces difficulties in accurately detecting and recognizing turn signals due to variations in lighting conditions and vehicle designs. Existing solutions often fall short as they rely on limited data and fail to adapt to diverse scenarios. This dataset aims to provide a comprehensive solution by offering a large collection of labeled images that can enhance model training for robust turn signal detection. Data is collected using high-resolution cameras in varying environments including day and night conditions. Quality control measures include multi-round annotations, consistency checks, and expert reviews to ensure high accuracy. The dataset is organized in JPG format for images, with structured labeling for easy access and analysis. The dataset contains 15,000 images, estimated at 3.2 GB in size, providing a rich resource for developing advanced detection algorithms.
Dataset Insights
Sample Examples
b3e23dde**.png|1280*1149|796.97 KB
89cb677e**.png|1280*1059|923.77 KB
d764d696**.png|1280*1019|1.24 MB
9221d2f6**.png|1280*1172|1.25 MB
cf0d19b9**.png|1280*1136|1.59 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| vehicle_type | string | The type of vehicle identified in the image, such as sedan, truck, SUV, etc. |
| indicator_status | string | The status of the turn signal, such as on, off, or malfunction. |
| indicator_position | string | Identify the position of the turn signals, whether it is on the left side, right side, or both sides. |
| occlusion_level | string | Evaluate the degree to which the turn signal is occluded, for example: none, partially, completely occluded. |
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 |
Frequently Asked Questions
- What types of images are included in the Turn Signal Detection Dataset?
- The Turn Signal Detection Dataset mainly includes images of vehicle turn signals in various environments.
- What industrial applications is the Turn Signal Detection Dataset suitable for?
- The dataset is suitable for industrial applications that require enhanced vehicle recognition and automation, such as autonomous driving and intelligent transportation systems.
- Which technologies can benefit from improved accuracy using the Turn Signal Detection Dataset?
- Using this dataset can improve the accuracy of object detection algorithms in detecting turn signals, contributing to the reliability of vehicle automation technologies.
- How can the Turn Signal Detection Dataset be used for algorithm training?
- Researchers can use this dataset to train and test object detection models to improve their performance and accuracy in detecting turn signals.
- What are the main advantages of the Turn Signal Detection Dataset?
- The main advantages are providing high-quality and diverse image data, which help improve the generalization and accuracy of detection models.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Turn Signal Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/802ee878c4bab415b397406006ec1881},
urldate={2025-08-28},
keywords={turn signal detection,automotive dataset,image classification,object detection,vehicle recognition},
version={1.0}
}Using this in research? Please cite us.
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