Lane detection requires precise pixel-wise identification and prediction of lane curves. Our Experts at rProcess have ability and technology to identify and track lanes for developing algorithms for driverless vehicles.
Our team is trained to draw and label bounding boxes for object detection, classification, and localization in images and videos to power the future of autonomous vehicles. We are built to rapidly scale up and down to meet your requirements.
We offer perfect solution for machine learning dataset. Our aerial imagery annotation services helps to build drone-based surveillance, topographical mapping, developing object detection and motion tracking.
Our expertise at rProcess are known to handle any kind of requirements related to computer vision applications, object detection. Our image tagging services makes data more structured and organized.
With semantic annotation, we offer better dataset for machine learning algorithms by providing additional information to a piece of data and semantic tagging.
To power the future of autonomous vehicles, we provide accurate and high-quality data labeling for AI technologies such as traffic video labeling, type of vehicle, road, sign boards, etc.
Realizing that artificial intelligence has the potential to be the most disruptive technology ever, we have got expertise in almost all aspects of AI which even includes Natural Language Processing.
Our highly skilled and experienced professionals detect objects, extract information and annotate the images and videos. And we deliver highest-quality results at a scalable service.
Our manual video annotation solution offers tracking of each object as they move throughout the video/frame. We guarantee highest accuracy of object tracking.
Offering high grade data labeling services for machine learning through image, video, text labelling and transcription to help create various AI algorithms to build right model for real-life use across diverse industries like autonomous machines, robotics, agriculture, automobiles, farming, healthcare & retail
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We provide Advanced driver assistance system services through LiDAR Annotation.
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LiDAR, or light detection and ranging, is a popular remote sensing method used for measuring the exact distance of an object on the earth’s surface. Even though it was first used in the 1960s when laser scanners were mounted to aeroplanes, LiDAR didn’t get the popularity it deserved until twenty years later. It was only during the 1980s after the introduction of GPS that it became a popular method for calculating accurate geospatial measurements.
There are three primary components of a LiDAR instrument — the scanner, laser and GPS receiver. Other elements that play a vital role in the data collection and analysis are the photodetector and optics. Most government and private organizations use helicopters, drones and airplanes for acquiring LiDAR data.
LiDAR follows a simple principle — throw laser light at an object on the earth surface and calculate the time it takes to return to the LiDAR source. Given the speed at which the light travels (approximately 186,000 miles per second), the process of measuring the exact distance through LiDAR appears to be incredibly fast. However, it’s very technical. The formula that analysts use to arrive at the precise distance of the object is as follows:
The distance of the object=(Speed of Light x Time of Flight)/ 2
LiDAR is divided into two types based on its functionality — Airborne LiDAR & Terrestrial LiDAR.
Airborne LiDAR is installed on a helicopter or drone for collecting data. As soon as it’s activated, Airborne LiDAR emits light towards the ground surface, which returns to the sensor immediately after hitting the object, giving an exact measurement of its distance. Airborne LiDAR is further divided into two types — Topological LiDAR and Bathymetric LiDAR.
Unlike Airborne, Terrestrial LiDAR systems are installed on moving vehicles or tripods on the earth surface for collecting accurate data points. These are quite common for observing highways, analysing infrastructure or even collecting point clouds from the inside and outside of buildings. Terrestrial LiDAR systems have two types — Mobile LiDAR and Static LiDAR.
Whether it is collected as discrete points or full waveform, most often LiDAR data are available as discrete points. A collection of discrete return LiDAR points is known as a LiDAR point cloud.
The commonly used file format to store LIDAR point cloud data is called .las which is a format supported by the American Society of Photogrammetry and Remote Sensing (ASPRS). Recently, the .laz format has been developed by Martin Isenberg of LasTools. The differences is that .laz is a highly compressed version of .las.
Data products derived from LiDAR point cloud data are often raster files that may be in GeoTIFF (.tif) formats.
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