Authors:
Johann Thor Mogensen Ingibergsson
1
;
Dirk Kraft
2
and
Ulrik Pagh Schultz
2
Affiliations:
1
CLAAS E-Systems and University of Southern Denmark, Denmark
;
2
University of Southern Denmark, Denmark
Keyword(s):
Safety, Functional Safety, Image Quality Assessment, Low-level Vision.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
Abstract:
Computer vision has applications in a wide range of areas from surveillance to safety-critical control of autonomous
robots. Despite the potentially critical nature of the applications and a continuous progress, the
focus on safety in relation to compliance with standards has been limited. As an example, field robots are
typically dependent on a reliable perception system to sense and react to a highly dynamic environment. The
perception system thus introduces significant complexity into the safety-critical path of the robotic system.
This complexity is often argued to increase safety by improving performance; however, the safety claims are
not supported by compliance with any standards. In this paper, we present rules that enable low-level detection
of quality problems in images and demonstrate their applicability on an agricultural image database. We hypothesise
that low-level and primitive image analysis driven by explicit rules facilitates complying with safety
standards, which i
mproves the real-world applicability of existing proposed solutions. The rules are simple
independent image analysis operations focused on determining the quality and usability of an image.
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