Halcon AI are the future
Identify items of interest with one or many bounding boxes
Mark individual points in an image
Annotate straight lines in an image
Annotate freeform lines in an image
Annotate objects with accurate width, depth, and height
Like bounding boxes, but with additional precision
Classify each pixel of an image
Measure, yaw, pitch, and roll of an item of interest
Organize your media with discrete categories
Halcon AI Visual Inspection simplifies the process of gathering and labeling data, training new AI models, and deploying those models at the edge.
Minimal hardware requirements and easy integration with existing data set
Customized live annotation workflow for time sensitive projects
Best-in-class data quality by mandating consensus and high worker accuracy
The functionality and accuracy of Halcon AI remain at the highest level in high-volume data annotation
Label both images & videos with added metadata to make the objects recognizable in data set
A mix of AI models and manual annotation for faster segmentation of object in the images with high accuracy
Dataset Management: Dataset is a structured collection of data. Managing the dataset increases the potential of the data and helps the organization make efficient and faster decisions. The import and handling of large file formats are to be efficiently handled by the tool.
Annotation Methods: Automated annotations sometimes result is anomalies and errors. Human-in-the-loop method is to be incorporated for better data quality and exception management. A team of data labelers can decide whether to enlarge or eliminate a bounding box while using pre-annotation to tag photos.
Data Quality Control: Data quality is to be administered for better accuracy, checking the relevance of the required data, check on the completeness, consistency, and timeliness of the data.
Workforce Management: A human workforce of a project is expected to understand the needs and guidelines of the project and train the workforce accordingly based on that.
Security: Most important aspect of dealing with data is maintaining its security. Reducing the risk of exposing the data and prevent breaches to ensure safe usage of secure data. The annotation tools should restrict the data downloads
An annotator leads the task of labeling content like images or video that makes it recognizable for machine learning models and use the same for further predictions. The annotator’s work is verified by the QA team. The QA team has the authority to return the labeling back to the annotator for redoing it, if they feel there is a compromise in the quality. Once the QA approves it, it is passed on to the Admin. The admin, once again, has the authority to disapprove and send it back to QA team. The QA redoes the work or finds another annotator. Data security and privacy are the major assets to be considered while approving. Once the Admin approves, the process is completed.
Human intelligence is gained from a variety of experiences, adaptation to surrounding environments, comprehending complex ideas etc. AI-driven applications on the other hand have higher executional rate, higher operational capacity and greater accuracy in the results. Both human intelligence and artificial intelligence have their own pros and cons. The analogous human mind and digital machines combined gives a greater performance at a very fast rate as compared to the usage of only human mind.
Raw data is requested for labeling by the annotation tool. The annotation tool is a combination of human intelligence and AI-assisted software. The result of this tool is passed further for AI-assisted labeling. The labeled, unverified data is discarded at this stage and the verified ones are further goes through a human auditing, the result of which is labeled, verified data.