Title
An Extended Modular Processing Pipeline For Event-Based Vision In Automatic Visual Inspection
Abstract
Dynamic Vision Sensors differ from conventional cameras in that only intensity changes of individual pixels are perceived and transmitted as an asynchronous stream instead of an entire frame. The technology promises, among other things, high temporal resolution and low latencies and data rates. While such sensors currently enjoy much scientific attention, there are only little publications on practical applications. One field of application that has hardly been considered so far, yet potentially fits well with the sensor principle due to its special properties, is automatic visual inspection. In this paper, we evaluate current state-of-the-art processing algorithms in this new application domain. We further propose an algorithmic approach for the identification of ideal time windows within an event stream for object classification. For the evaluation of our method, we acquire two novel datasets that contain typical visual inspection scenarios, i.e., the inspection of objects on a conveyor belt and during free fall. The success of our algorithmic extension for data processing is demonstrated on the basis of these new datasets by showing that classification accuracy of current algorithms is highly increased. By making our new datasets publicly available, we intend to stimulate further research on application of Dynamic Vision Sensors in machine vision applications.
Year
DOI
Venue
2021
10.3390/s21186143
SENSORS
Keywords
DocType
Volume
event-based vision, automatic visual inspection, dynamic vision sensors, object classification
Journal
21
Issue
ISSN
Citations 
18
1424-8220
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Moritz Beck100.34
Georg Maier2328.24
Merle Flitter300.34
Robin Gruna4144.50
Thomas Längle500.34
Michael Heizmann6219.11
Jürgen Beyerer731575.37