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Drive the future of Autonomous vehicles with  Data Annotation from TagX.

TagX annotation services can play a crucial role in developing AI-based automotive applications and self-driving technology. With the help of our expert annotators and advanced annotation tools, we can provide accurate and precise labeling for various types of data such as images, videos, Lidar data, and more. Our annotation services can help train machine learning models to recognize and classify different objects, including road signs, traffic signals, pedestrians, and vehicles.

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Data Annotation for Autonomous Driving - Use Cases

Autonomous vehicles

With the future of the automotive industry moving towards autonomous vehicles, high-quality datasets are vital in creating safe and accurate models. Bounding boxes, polygons, full semantic segmentation, and cuboids drawn on 2D images are all techniques that can be used here.

Traffic Monitoring

Traffic monitoring allows autonomous vehicles to perceive, understand and predict the behavior of other road users, such as other vehicles, pedestrians, and bicycles, to ensure safe and efficient operation. By using data annotation from TagX, traffic monitoring systems can be trained to recognize a wide range of traffic scenarios and road users, such as different types of vehicles, pedestrians, and bicycles. This can help autonomous vehicles to operate safely and efficiently in a variety of traffic conditions.

Lane Detection

Lane detection in autonomous driving is used to detect the location and boundaries of lanes on the road. This information is crucial for the safe operation of autonomous vehicles, as it allows the vehicle to stay within its lane and avoid collisions. TagX can perform data annotation on road scenes, identifying specific attributes such as the location and boundaries of lanes, traffic signs, and road markings.

In-cabin monitoring

Data annotation plays a crucial role in the development and training of in-cabin monitoring systems. The process involves labeling and tagging images and videos of the interior of the vehicle, identifying specific attributes such as the location and posture of passengers and the driver, as well as facial expressions, gestures, and even emotions. This labeled data is then used to train machine learning models that can automatically analyze new images and videos of the interior of the vehicle and detect any potential issues.

Automated Driver Assistance System

Automated Driver Assistance System (ADAS) refers to the use of computer vision and AI technologies to provide drivers with advanced features such as lane departure warnings, automatic braking, and adaptive cruise control. By providing high-quality, annotated data, TagX can help autonomous vehicle manufacturers to develop accurate and reliable ADAS systems that can improve safety and efficiency. By using data annotation, ADAS systems can be trained to recognize a wide range of road scenarios and road users, such as different types of vehicles, pedestrians, and bicycles, as well as different types of road markings, traffic signs, and weather conditions.

Types of Annotation

With Tagx, your AI applications will recognize and identify objects at unmatched speed, improving your models predictions and confidence levels. We understand that different AI use cases require different types of data annotation to achieve optimal performance. TagX offers a wide range of annotation services for image, text, audio, and video data, as well as 3D point cloud and LiDAR annotation. We pride ourselves on delivering high-quality annotations with precision and accuracy to ensure that your AI models can perform at their best. Whatever your use case may be, you can count on us to provide the right type of annotation to meet your specific needs.

Bounding Box Annotation
Polygon Annotation
Polyline Annotation
3D Cuboids
Semantic Segmentation
Point Annotation
Video Annotation

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