Intelligent Quality Assurance Process

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Problem Description

Kognic provides a unique platform for ground truth annotation. One of our main commitments to our customers is about the data quality we deliver. As of today, our data quality process relies on a mix of manual and automated work.

Kognic is interested in developing innovative quality assurance tools based on artificial intelligence and machine learning. Some initial work has been carried out on classification tasks and object detection in order to detect the annotation mistakes in a dataset, but a lot of efforts and challenges remain:

One is about the amount of paradigms we must handle. Our clients will contact us with requests to annotate and quality assure data coming from cameras, radars, lidars, IMU, etc. These sensors also have different technical specifications: resolution, point density, effective range, field of view, etc. In addition, we support a multitude of annotation types: 2d boxes, 3D cuboids, 2D/3D semantic segmentation, curves, etc. We also need to track semantic properties, such as an object's color, shape, state, etc.

In order to be able to perform automatic data quality checks, a large amount of AI algorithms must be developed. They must support different kinds of inputs and be able to adapt to the complexity and subtleties of the detection tasks.

The first topic of interest for us is to improve our existing workflow and develop new automatic quality tools. The second is to understand how the difference in the annotation task (sensor, annotation type) can impact the quality process.

We are mainly interested in exploring solutions for quality assurance for 2 tasks:

3D data (lidar)

Sequence of frames (2D or 3D)

We expect students to:

Perform a thorough literature study and select one or two suitable solutions to perform quality assurance

Participate in the selection and preprocessing of required data necessary for the solution

Implement and validate the selected solutions

Suggest an effective way to implement the developed solution to Kognic's platform

Applicant profile

We seek two students with an interest in computer vision and deep learning (CNN, RNN). We also expect the students to have experience with Python and preferably Pytorch framework.

Project information

Project start: 16th Jan 2023

Duration: 20 weeks

Last date of application: 5th of Dec


The remuneration for this internship will be 1000 SEK/week (before taxes), which will be paid in whole when the thesis is completed and we have received a copy.

The total remuneration will be 20000SEK for the project per student.

Contact information:

Andreas Falkovén,
Tommy Johansson,

Reference / Links

[1] Measuring data quality efficiently

[2] Dataset Quality Analytics

[3] Why (even random) annotation errors are problematic for perception


Master Thesis Proposals