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Developing autonomous driving technologies heavily relies on machine learning to achieve the best outcome and outdo the competitors. The computer vision system of all self-driving vehicles has to be trained and tuned with a large amount of structured, annotated & labeled data. We at Dodeed AI end-to-end data labeling services paired with full-time data annotation experts deliver high-quality, error-free, human-labeled, and cost-effective AI training data for autonomous vehicles.
3D point cloud annotation allows you to visualize an object for more detailed detection in order to get the dimension exactly correct.
Detecting vehicles, vulnerable road users, traffic signs, traffic lights, etc for self-driving cars and smart cities, AI models rely on accurately annotated and labeled data to enhance object detection precision. Dodeed's data expertise helps build robust computer vision systems for self-driving vehicles.
Data annotations for In-Cabin/Driver or Occupant Monitoring Systems involve precise labeling of facial expressions, eye movements, head orientation, and body posture. These annotations are essential for training AI models to recognize drowsiness, distractions, or risky behaviors, improving driver assistance and enhancing passenger safety in both autonomous and semi-autonomous vehicles.
At Dodeed AI, we deliver precise labels for facial expressions and body posture, enhancing AI model accuracy to detect fatigue and distractions, improving safety in autonomous vehicles
Our Automated License Plate Recognition (ALPR) and Traffic Analysis data annotation services provide precise labeling of vehicle license plates, traffic signs, and road conditions. This enables AI systems to accurately identify license plates, monitor traffic patterns, and improve road safety.
Our expertise ensures high-quality data for enhanced vehicle tracking, traffic flow optimization, and effective enforcement of traffic regulations in smart cities and transportation networks.
Parking Management data annotation services offer precise labeling of parking spaces, vehicle locations, and entry/exit points, enabling AI systems to monitor availability and optimize operations.
With accurate data, parking solutions become more efficient, reducing congestion and improving traffic flow. This service supports smart parking technologies, enhancing user experience and ensuring seamless parking management in urban environments.
Custom Use-cases data annotation services are tailored to meet the unique needs of specialized projects. Whether you're working on advanced AI models for niche industries or handling complex datasets, our expert team provides precise annotations that align with your specific requirements.
From healthcare imaging to geospatial data or autonomous systems, we offer flexible solutions to ensure your AI models are trained with the highest accuracy and reliability, empowering innovation across diverse sectors.
Developing autonomous driving technologies heavily relies on machine learning to achieve the best outcome and outdo the competitors. The computer vision system of all self-driving vehicles has to be trained and tuned with a large amount of structured, annotated & labeled data. We at Dodeed AI end-to-end data labeling services paired with full-time data annotation experts deliver high-quality, error-free, human-labeled, and cost-effective AI training data for autonomous vehicles.
3D Point Cloud Annotation for LiDAR Sensing is essential in the AV and ADAS sectors, transforming LiDAR data into detailed three-dimensional representations of the environment. This technique captures precise spatial information about objects, terrain, and obstacles, enabling AI systems to analyze surroundings effectively.
By leveraging 3D point clouds, autonomous vehicles gain enhanced perception and situational awareness, improving navigation accuracy and safety in complex driving scenarios.
Bounding Box Annotation for Object Detection and Tracking is critical in the AV and ADAS industries. By encapsulating objects like vehicles and pedestrians in rectangular boxes, this method enhances real-time recognition and tracking.
Simplifying visual data into clear structures improves detection accuracy, leading to safer navigation and effective decision-making in dynamic driving environments.
Providing precise 3D bounding boxes around objects such as vehicles, pedestrians, and obstacles. This technique enables better spatial awareness and depth perception for AI systems, facilitating accurate object localization and distance estimation.
By delivering detailed geometric information, 3D cuboid annotations enhance the safety and effectiveness of autonomous navigation and advanced driver assistance systems.
Semantic Segmentation for Object Classification is crucial for autonomous vehicles and ADAS, offering pixel-level labeling of roads, pedestrians, vehicles, and traffic signs. This enhances AI's ability to understand scenes and make informed decisions.
With precise object classification, autonomous systems can navigate safely and make real-time decisions, enhancing overall performance and safety in complex driving environments.
Polygon Annotation for Irregular Shapes provides accurate labeling of non-uniform objects, critical for autonomous vehicles and ADAS solutions. By precisely outlining road signs, pedestrians, vehicles, and other irregular shapes, this service enhances object detection and scene understanding.
It enables AI models to better navigate complex environments, improving the safety and reliability of autonomous driving systems.
We adapt quickly to your evolving project needs. Whether you're launching a proof of concept or scaling to millions of annotations, our agile workflow ensures speed, flexibility, and consistent quality — without the bottlenecks
Your data is your asset — and we treat it that way. We follow strict data privacy protocols, secure infrastructure practices, and industry-grade compliance standards to ensure your information stays protected at every step.
From emerging startups to global enterprises, we offer scalable pricing models that align with your budget and goals. You get top-tier annotation quality — without the enterprise-only price tag.
Our efficient data annotation process guarantees quality at every stage. We prepare and clean datasets, apply precise labeling through skilled annotators, and conduct thorough quality checks. Finally, we deliver annotated datasets ready for AI model training and deployment.
Requirement Analysis is a crucial step where we collaborate closely with clients to fully understand their project goals and annotation needs. This phase allows us to define the types of annotations (e.g., bounding boxes, polygons, 3D cuboids) and quality benchmarks that align with their AI model objectives. We assess the data, develop detailed guidelines, and establish clear workflows to ensure that our annotations meet the highest standards.
By conducting thorough requirement analysis, we deliver tailored, accurate data labeling solutions that accelerate AI training and deployment.
In the Annotation Process, we leverage our expert annotators and advanced tools to label data with precision and accuracy. Depending on the project requirements, we apply various annotation techniques such as bounding boxes, semantic segmentation, or 3D cuboids. Our team follows the established guidelines to ensure consistency across all annotations, while adhering to quality standards.
Throughout the process, we maintain a seamless workflow, ensuring that the labeled data is ready for immediate use in AI model training and development.
In the Quality Assurance phase, we conduct thorough evaluations to guarantee the accuracy and uniformity of annotations. Our team uses both manual inspections and automated tools to catch any errors or inconsistencies. By implementing multi-layered reviews, we uphold stringent quality standards, ensuring all annotations align with project specifications.
This meticulous process is vital for delivering high-quality data that enhances the effectiveness and reliability of AI model training.
The Feedback Loop is an integral part of our process, where we continuously refine and improve annotations based on client feedback and quality assessments. By analyzing the results of quality checks and incorporating insights from clients, we adjust guidelines and workflows to enhance accuracy and efficiency.
This iterative approach allows us to respond quickly to evolving project needs, ensuring the final annotated data consistently meets the highest standards for AI model development.
In the Finalization and Delivery stage, we ensure all annotations are polished and meet the agreed-upon standards. After passing thorough quality checks and revisions, the annotated data is formatted and packaged according to client specifications. We prioritize timely delivery, providing the final dataset ready for immediate use in AI model training and deployment.
This phase ensures the project is completed efficiently, with accurate and high-quality data that drives optimal AI performance.
Our clients share how Intellisane AI’s precise and reliable annotation services boosted their AI projects, showcasing our commitment to quality and trust.
Intellisane AI played a key role in helping us reach 97% accuracy in automating foundation layout detection. Their deep understanding of spatial data and labeling precision brought measurable improvements to our AI pipeline. The team was communicative, detail-oriented, and delivered everything ahead of schedule.S. RagavanSr. ML Engineer
Our fashion AI model required pixel-level segmentation across 72 garment categories—and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.Valerio ColamatteoSr. AI Scientist & Team Lead
For our ADAS project, Intellisane AI delivered precise vehicle annotations across diverse traffic scenes, including multiple object classes and occlusion scenarios. Their expertise in automotive data workflows and quality-first mindset helped us pass all validation checks, with timely delivery and professional communication throughout.Raphael LopezCo-Founder & CTO
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