Our AI solutions process images and video, recognize objects and scenes, and generate descriptions and labels for visual content. In other words, thanks to computer vision, you never lose sight of your business value.
We develop custom, high-performance machine learning models for object detection, even in challenging real-world circumstances. We tailor our approach - data pre-processing, modeling, tuning and setup - to fit each unique use case for optimal results.
Unlock the full potential of your video data with our expert object tracking techniques. We help you detect and segment objects, identify activities, and overcome the unique challenges of video analysis, like resource management and model architecture.
Edge Processing of video can reduce network traffic and increase data security. With our Edge Video Analysis (EVA) project, subsidized by Vlaio, we aim to develop a high-performing solution for anonymizing and re-identifying video on the edge.
Monitor, steer and improve your quality control with advanced machine vision technology. Our algorithms detect various defects, optimizing production processes.
Discover how our expertise in Computer vision leads businesses to success
By embedding artificial intelligence in its platforms, Keypoint is able to reduce fraud cases and will guarantee all home repairs are completed properly and at a fair price. Next to that, water damage claims will be prevented through the implementation of smart sensors and AI.
December 30, 2021
ML6 helped Accolade Wines to implement a ML process to capture real time insights during the manufacturing process and prevent wine loss from happening with predictive analytics, enabling all 3 bottling lines to further reduce losses and saving another 1 million litres of wine per year or the consumption of 50 000 people per year. A 200% ROI for Accolade Wines.
September 8, 2021
ML6 helped to move an algorithm to a multispectral camera. ML6 helped downscale and optimize the algorithm and helped get it embedded by working together with the camera provider. Through the collaboration, CNH was able to go into production much faster. They now also have the necessary expertise to deploy algorithms on an embedded system for future use cases.
March 5, 2021
Building a computer vision solution can be complex process. Let's take a look at some common challenges.
Computer vision algorithms need labeled data to learn, but getting this data can be difficult and expensive. That’s why it’s important to use the data effectively, which means getting good quality data and making sure it covers a wide range of situations. This leads to more accurate and robust computer vision models.
Finding and separating objects in an image is important for many computer vision tasks. It can be challenging to track objects over time, especially if they're hidden or have complicated shapes, but there are ways to overcome these challenges and improve the accuracy of computer vision models.
Computer vision aims to accurately and efficiently identify objects in images, even if some objects are hard to tell apart. With proper training on large datasets, computer vision models can achieve high accuracy and improve our ability to analyze visual information.
Choosing the right model is crucial for successful computer vision solutions. It requires a deep understanding of algorithms and fine-tuning for specific use cases. While transfer learning can be useful, adapting pre-trained models to new domains is important for optimal results.
Computer vision applications often need to process data quickly, but the algorithms can be demanding. This means that it's important to find ways to make the algorithms faster without losing accuracy. Finding the right balance between speed and accuracy is key.
Explaining how computer vision models work can be tough because they often use complex neural networks. But there are ways to make these networks more interpretable, so stakeholders can understand and use the models more effectively.
To start building a computer vision solution, you first need to collect and prepare data. This includes cleaning and annotating images or videos, and gathering a diverse dataset that represents all possible cases.
Convolutional Neural Networks (CNNs) and Transformer-based algorithms are commonly used models for computer vision. These models are designed to detect and analyze features and patterns in images using specific architectures.
For the computer vision model to work, it must be trained using the collected data. This process includes minimizing the error between predicted and actual results, which requires powerful hardware, such as GPUs.
After the model is trained, it is evaluated to see how well it performs using various evaluation metrics such as precision, recall, mIoU and mAP.
Once trained and evaluated, the model can be used to perform tasks such as image classification, object detection, and image segmentation. Depending on the resources and infrastructure available, deployment can be on-premise or in the cloud.
When new data becomes available or the model's application changes, it may need to be fine-tuned or adapted. Retraining the model on new data or updating its parameters can be part of this process.
Get in touch to discover how our computer vision solutions can transform your business operations