Data annotation & labeling blog.

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Data annotation

Data annotation

Data annotation is one of the important parts of machine learning, especially in AI example. To know more about this and get better knowledge on Data Annotation, we need to know ALL the basics! So, we will be putting up a blog on "Data Annotation" [AIN-103] with A - Z coverage which will help you in understanding different parts related to Annotation of data.

Labeling Occluded Objects for Autonomous Systems

Labeling Occluded Objects for Autonomous Systems

For an autonomous car, the ability to recognize hidden objects is one of the most difficult skills that distinguishes a simple program from true intelligence. In the real world, we almost never see objects perfectly whole: a car in front obscures part of a pedestrian, a road sign hides behind

Labeling Autonomous Vehicle Data in Low Light and Infrared

Labeling Autonomous Vehicle Data in Low Light and Infrared

Labeling data for autonomous vehicles in low-light conditions and utilizing infrared radiation is a crucial step in developing safe autonomous driving systems. Night scenes present unique challenges for sensors, but traditional cameras often lose contrast and detail, and objects can remain poorly visible or distorted due to glare and shadows.

Annotating Autonomous Vehicles in Adverse Weather: Rain, Fog & Snow Scenarios

Annotating Autonomous Vehicles in Adverse Weather: Rain, Fog & Snow Scenarios

The development of autonomous vehicles is a key area of modern research in the fields of AI and computer vision. One of the primary conditions for the safe operation of such systems is the ability to accurately perceive the environment under various operating conditions. Adverse weather phenomena, particularly rain, fog,

Camera-LiDAR Calibration: Multi-Sensor Alignment for Autonomous Vehicles

Camera-LiDAR Calibration: Multi-Sensor Alignment for Autonomous Vehicles

Modern cars are often equipped with a variety of sensors, such as cameras, LiDAR, and radar, each providing unique information about the environment. The real challenge is integrating data from disparate sensors into a single, coherent map of the environment. Calibrating cameras and LiDAR is a crucial process for achieving

Labeling High-Resolution Radar Point Clouds for AV

Labeling High-Resolution Radar Point Clouds for AV

While cameras depend on lighting and LiDAR loses effectiveness due to beam scattering in water droplets or steam particles, high-resolution radars ensure stable operation in the most difficult meteorological conditions. The ability of radio waves to pass through fog, rain, and snow makes this sensor indispensable for safe movement in

Abstract of Multimodal Sensor Data Fusion

Abstract of Multimodal Sensor Data Fusion

Multimodal data fusion from sensors such as cameras, LiDAR, and radar is a component of a modern autonomous perception system. Each sensor has its own advantages. Cameras provide detailed visual and semantic information, LiDAR generates accurate 3D scene geometry, and radar displays objects in difficult weather conditions and at long

Time-Series Data Labeling for Self-Driving Cars

Time-Series Data Labeling for Self-Driving Cars

For an autonomous car to move safely through a city, simply recognizing objects around it is not enough. It must understand time and the sequence of events. In the world of autonomous driving, data does not exist as a set of random photos – it consists of continuous information streams where