Driving Recorder Image

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Project Overview:

Objective

The main goal of this project is to collect and label images from driving recorders. These images are important for developing advanced driving assistance systems (ADAS) and self-driving technologies. By labeling these images, we aim to boost the accuracy of object detection, lane recognition, and traffic sign interpretation. Consequently, this will enhance road safety and driving efficiency.

Scope

This project aims to gather a wide range of driving recorder images from various conditions, including urban and rural areas, as well as different weather scenarios. Moreover, these images will be annotated to identify several elements such as vehicles, pedestrians, road signs, and lane markings.

Sources

Automotive Partnerships: We collaborated with numerous automotive companies, acquiring 12,000 high-quality images from their driving recorders.
Real-World Drives: Our team conducted extensive real-world driving sessions, adding 5,000 unique images depicting a variety of road situations and obstacles.
Public Resources: We further enriched our collection with 3,000 carefully selected and annotated images from public databases to ensure a well-rounded dataset.

Data Collection Metrics

Total Images: 20,000
From Automotive Partnerships: 12,000
Real-World Driving Sessions: 5,000
Public Databases: 3,000

Annotation Process

Stages

Event Categorization: Each image was meticulously tagged with relevant events such as traffic light recognition, pedestrian detection, and road sign identification.
Environmental Context: We included detailed information for each image, such as weather conditions, time of day, and GPS coordinates.
Obstacle identification: primarily focused on marking various obstacles, including static ones like potholes and dynamic ones like pedestrians.

Annotation Metrics

Images with Event Annotations: 20,000
Contextual Data Enriched: 20,000
Obstacle Tags Added: 20,000

Quality Assurance

Stages

Expert Review: Traffic and vehicle experts often review a subset of annotated images to ensure accuracy and reliability.
Continuous Improvement: We regularly update and enhance our annotation techniques. These updates take into account the ever-changing road and driving conditions.
Feedback Loop: We work closely with developers of autonomous driving systems to gather their feedback. Consequently, we make ongoing improvements based on their insights.

QA Metrics

Expert Review Cases: To maintain high-quality annotations, 15% of the annotated images are reviewed by experts. This helps in identifying and correcting any inconsistencies or errors.
Accuracy Improvement Rate: We track and aim to improve the accuracy of annotations on a quarterly basis. Regular reviews and feedback loops ensure continuous enhancement of our annotation quality.

Conclusion

The “Driving Recorder Image Collection and Annotation” project is essential for improving ADAS and autonomous driving technologies. By collecting and labeling driving images, it gives important data for machine learning models. As a result, these models can better understand and navigate real-world driving situations. In addition, this project represents a big step toward safer and more efficient driving. Moreover, this initiative ensures the data is broad and varied, covering different driving conditions and scenarios. Thus, the project contributes to making driving experiences safer and more reliable.