AMDC has been actively working on developing ML models to perform image classification and object detection for various applications.
In Computer Vision the term Image Classification refers to the process of assigning a certain class to an image based on the visual content.
AMDC customly develops task-specific Image Classifiers based on the latest international research results. Our particular focus is on achieving extremely low execution time on mobile embedded hardware, such as NVidia Jetson or the latest FPGAs.
Object Detection is the process of localizing and classifying certain objects within an image.
AMDC Object Detection is based on state-of-the-art Object Detection Model Architectures, such sas YOLO, SSD or FasterRCNN, which we carefully modify and customly train to satisfy the challenging requirements of our customers. Our models are specifically optimized to provide a very high accuracy and, at the same time, allow for sufficient refresh rates on current embedded systems, such as NVidia Jetson Modules or the latest FPGAs.
Image super resolution is an actively researched topic in Computer Vision. Image Super-resolution is the process of enhancing the quality of the image by increasing its resolution using deep learning methods.
When Image-super resolution is paired with object detection, the number of objects detected in the super resolved images is more than the number of objects detected in the native resolution. It is also very useful in increasing the quality of the dataset.
AMDC is working with a number of state-of-the-art super resolution algorithms to produce high quality images for various applications.