deep-learning

Real-Time Industrial Inspection

Topics Real-time monitoring in Additive Manufacturing Real-time monitoring in Additive Manufacturing In additive manufacturing, items are 3D printed layer-by-layer using materials like plastics, polymers, and metals. Unfortunately, instabilities in the printing process can produce defects like cracks, warping, and pores/voids within the printed item. Our goal at IPI is to develop computer vision systems that identify the creation of these defects in real time, then provide the 3D printer with sufficient information to intervene in a way that corrects, or avoids, the defect. Since these defects can occur over a very short amount of time (< 1 ms), our monitoring systems need to operate at very high speeds while also providing accurate results. Melt pool monitoring: GPU-based real-time detection of pore defects using dynamic features and machine learning Using high speed cameras and photodiodes (sampling rates > 20 kHz), we are exploring AI models that can highlight defect creation.

Online Talk: Cooperative sensor fusion for detection and tracking

Watch IPI researcher ​​​​​​​David Van Hamme talk about Cooperative Sensor Fusion research

IPI joins Industry Leaders in AI for Manufacturing Webinars

Brian Booth joined industry leaders earlier this year to speak on the use of AI in additive manufacturing workflows

Flanders AI

Groundbreaking artificial intelligence research enabling a meaningful impact on people, industry and society. IPI researches real-time and power-efficient AI in the edge for various applications. National project (Flemish EWI), 7/2019 – 6/2022

Information feedback loop for improved pedestrian detection in an autonomous perception system

Environmental perception systems for autonomousvehicles are often built using heterogeneous technologies thatoperate in a sequential manner. In the task of object trackingin particular, where the classical detector-tracker interactionis a serial …

Learning morphological operators for depth completion

Depth images generated by direct projection of LiDAR point clouds on the image plane suffer from a great level of sparsity which is difficult to interpret by classical computer vision algorithms. We propose a method for completing sparse depth images …

ACHIEVE

Researchers in ACHIEVE are designing highly integrated hardware-software components for the implementation of ultra-efficient embedded vision systems as the basis for innovative distributed vision applications For IPI, the first goal of this project is to design algorithms for distributed multiple targets tracking through a decentralized approach. The second goal is to improve object detection and tracking using a multi-sensor approach. Thermal cameras have promising potential in surveillance applications, especially when combined with optical cameras. The third goal of the project is to provide solutions for behaviour analysis and action recognition. The research will use high-level analysis to automatically determine which cameras observe the same or similar action, such as pedestrians waiting to cross the street. Deep learning is a promising approach. H2020-MSCA-ITN, 10/2017 – 9/2021

Exploiting reflectional and rotational invariance in single image superresolution