Geforce 256

Argos Solutions' plate scanners detect surface flaws, sort by quality, and aim to save time by optimizing image preprocessing on GPUs.

About us

We are a group of five enthusiastic data students who have received an exciting project from Argos Solutions AS regarding GPU-based image processing analysis.

Challenge

Argos Solutions provides scanners for plate manufacturers in the building materials and furniture industries. The function of these scanners is to detect defects on surfaces and sort according to quality criteria. The scanners incorporate line scanning cameras that deliver a continuous stream of images. Argos aims to leverage GPU in image processing to optimize and offload the workload on the CPU in their scanning processes. The increasing complexity associated with image processing has created a need for a more efficient solution. Argos acknowledges that traditional CPU approaches may be limiting and therefore views GPU as a potential solution to enhance processing efficiency. By exploring the transition from CPU to GPU, Argos seeks to address the technical challenges associated with handling image data and difference calculations.

Our Team

Sevag Hajji Narnian

Software Engineer

Group Leader

Ali Hessen

Software Engineer

Documentation

Siem Ghebre Ghebrehiwet

Software Engineer

Product Owner

Iver Enget Nesbø

Software Engineer

Scrum Master

Abdiqani Abdullahi

Software Engineer

Risk Analyst

Sprint 1

In our inaugural sprint, our focus was on project planning, culminating in our initial presentation. We made significant progress, selecting Scrum as our project management model. We clarified project requirements, developed a risk template, and presented our project plan, effectively communicating our approach to stakeholders

Sprint 2

We quickly realized that we had underestimated the importance of thorough technical planning in our project. Even though we had documentation of the algorithms, we soon discovered that this alone was not sufficient to ensure smooth progress. As we began to explore UML modeling, it became evident that we hadn't placed enough emphasis on establishing a solid technical framework before delving into the details. By ending early, we were able to reflect on our mistakes and recognize the need for better planning.

Sprint 3

We shifted our focus towards refining the algorithms and set a goal to delve deeper into the technical aspects of the project. We realized that to achieve this goal, it was crucial to commence work on implementation of the algorithm while meticulously documenting every step of the UML design process.