Risk Detection

Dataset for Training YOLOv11 AI Model to Detect Risky Objects for Assistive Guidance.

Objective

The goal of this project is to create a comprehensive dataset to train an AI model using YOLOv11 (You Only Look Once version 11). The model will detect and identify "risky objects" that blind and visually impaired individuals may encounter in indoor and outdoor environments. This dataset serves as the foundation for an assistive technology tool designed to enhance mobility and safety by providing real-time object detection and guidance.

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Dataset Overview

Targeted Objects

Objects identified as potentially risky were selected through research and user studies. The dataset focuses on items that could obstruct paths, pose tripping hazards, or cause injury if unnoticed.

Examples include:

• Outdoor Risks:

  • Vehicles
  • Bicycles
  • Potholes
  • Curbs
  • Barriers
  • People

• Indoor Risks:

  • Chairs
  • Tables
  • Shelves
  • Furniture (e.g., cabinet, closet)

Key Features of the Dataset

• Comprehensive Annotations:
  Every image is annotated with bounding boxes and labeled with object categories.
• Diverse Scenarios:
  Images are captured under varied lighting conditions, environments (urban, suburban, rural), and angles to ensure robustness.

Dataset Structure

1. Total Images: x labeled images.
2. Categories: x object classes identified as risky.
3. Formats: Compatible with YOLO input formats and others.

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Benefits of the AI Model

Real-Time Hazard Detection

The YOLOv11 model will process visual data from a wearable or smartphone camera, identifying and alerting the user to risks in real-time.

Improved Independence

By providing proactive guidance, the system empowers blind and visually impaired individuals to navigate more independently and safely.

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Technical Details

Model Training

• YOLOv11 architecture optimized for high accuracy and real-time performance.
• The dataset will be split into:
  1. Training (70%)
  2. Validation (20%)
  3. Testing (10%)

Augmentation Techniques

• Data Augmentation:
  Techniques such as rotation, scaling, and brightness adjustments to increase dataset robustness.
• Simulated Obstacles:
  Simulations of real-world obstacles (e.g., occlusion, partial object visibility).

Evaluation Metrics

• Precision
• Recall
• F1-Score
• mAP (mean Average Precision)

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Conclusion

This project aims to leverage advanced AI technology to address the unique challenges faced by blind and visually impaired individuals. By creating a specialized dataset for training YOLOv11, the model can detect risky objects with high precision, enhancing safety and mobility. The ultimate outcome is an AI-powered assistive system that provides greater independence and confidence to its users in their everyday lives.

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Credits and Acknowledgments

This project incorporates images from the following public datasets. We extend our gratitude to the creators and contributors of these datasets for making their work freely available to the research community:

1. Dataset Name 1
   • Description of the dataset (e.g., type of images, purpose).
   • License: CC BY 4.0.

We adhere to the terms and conditions of these datasets' licenses and greatly appreciate their contribution to advancing research in AI and assistive technologies.

                        @misc{
                            risk-detection-1_dataset,
                            title = { risk-detection-1 Dataset },
                            type = { Open Source Dataset },
                            author = { PBL5MU },
                            howpublished = { \url{ https://universe.roboflow.com/pbl5mu/risk-detection-1 } },
                            url = { https://universe.roboflow.com/pbl5mu/risk-detection-1 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { dec },
                            note = { visited on 2024-12-19 },
                            }