LTS QA

“We enjoy working with LQA because of the high quality of their work and their flexibility in accommodating any new task. In the past year, we had a variety of different projects, from simple bounding box annotations to complex pixel-wise segmentation, and every time the team was able to perform the task according to the specification and within the agreed time frame. We are very impressed with the amount of effort the team put into understanding of precise requirements and making sure there are no grey areas in the task before starting the work. The work processes seem very smooth and well organised, making the interactions easy and predictable. So far LQA have been one of our best experiences when working with external annotation teams.” – Daedalean

“Daedalean (www.daedalean.ai) was founded in 2016 with an aim to specify, build, test and certify a fully autonomous sensor and autopilot system that can reliably and completely replace the human pilot. Currently the company is working with EASA on an Innovation Partnership Contract to develop concepts of design assurance for neural networks.”

If you have any difficulties in data collecting or data annotation for your projects, feel free to reach out to us!

Lotus Quality Assurance (LQA)

Tel: (+84) 24-6660-7474
Email: [email protected]
Website: https://www.lotus-qa.com/

Data Annotation is the process of labelling the training data sets, which can be images, videos or audios. Needless to say, AI Annotation is of paramount importance to Machine Learning (ML), as ML algorithms need (quality) annotated data to process. 

In our AI training projects, we use different types of annotation. Choosing what type(s) to use mainly depends on what kind of data and annotation tools you are working on.

Bounding Box: As you can guess, the target object will be framed by a rectangular box. The data labelled by using bounding boxes are used in various industries, and most used in automotive vehicle, security and e-Commerce industries. 

Polygon: When it comes to irregular shapes like human bodies, logos or street signs, to have more precise outcome, Polygons should be your choice. The boundaries drawn around the objects can give an exact idea about the shape and size, which can help the machine make better predictions.

Polyline: Polylines usually serve as a solution to reduce the weakness of bounding boxes, which usually contain unnecessary space. It is mainly used to annotate lanes on road images.

3D Cuboids: The 3D Cuboids are utilized to measure the volume of objects which can be vehicles, buildings or furniture. 

Segmentation: Segmentation is similar to polygons but more complicated. While polygons just choose some objects of interest, with segmentation, layers of alike objects are labeled until every pixel of the picture is done, which leads to better results of detection.

Landmark: Landmark annotation comes in handy for facial and emotional recognition, human pose estimation and body detection. The applications using data labeled by landmark can indicate the density of the target object within a specific scene. 

If you have any difficulties in data collecting or data annotation for your projects, feel free to reach out to us!