Dmvi4 on ViCoS Lab

3DSR

Mon, 01 Jan 0001 00:00:00 +0000

CeDiRNet

Mon, 01 Jan 0001 00:00:00 +0000

CeDiRNet-3DoF

Mon, 01 Jan 0001 00:00:00 +0000

DAVE

Mon, 01 Jan 0001 00:00:00 +0000

DSR

Mon, 01 Jan 0001 00:00:00 +0000

SegDec-Net++

Mon, 01 Jan 0001 00:00:00 +0000

SuperSimpleNet

Mon, 01 Jan 0001 00:00:00 +0000

TransFusion

Mon, 01 Jan 0001 00:00:00 +0000

ViCoS Cube

Mon, 01 Jan 0001 00:00:00 +0000

The ViCoS Cube is a modular demonstration-cell application developed for public and professional presentation of deep-learning-based computer vision in practical scenarios. It was designed as a complete demonstrator that connects camera input, a graphical user interface, application control, and deployable deep-learning models, making research results easier to present outside the laboratory.

Associated links

Code main app: https://github.com/vicoslab/cube
Code demo programs: https://github.com/vicoslab/cube-apps
Code cameras: https://github.com/vicoslab/cube-cameras
Paper: Demonstration Cell for Showcasing Deep Learning in Practical Applications

Overall system structure

The system is organized as a modular stack with clearly separated responsibilities:

ViCoS Towel Dataset

Mon, 01 Jan 0001 00:00:00 +0000

Mixed SegDec-Net

Mon, 01 Jan 0001 00:00:00 +0000

Kolektor Surface-Defect Dataset 2 (KolektorSDD2 / KSDD2)

Mon, 01 Jan 0001 00:00:00 +0000

The dataset is constructed from images of defected production items that were provided and annotated by Kolektor Group d.o.o.. Various types of defects were observed on the surface of the item. The images were captured in a controlled industrial environment.

The dataset consists of:

356 images with visible defects
2979 images without any defect
image sizes of approximately 230 x 630 pixels
train set with 246 positive and 2085 negative images
test set with 110 positive and 894 negative images
several different types of defects (scratches, minor spots, surface imperfections, etc.)

DOWNLOAD HERE

Several different types of surface defects present in images: