Commonly, visual inspection tasks in the textile industry are performed by human experts. The major drawback
of this type of inspection is the human subjectivity, which affects accuracy and repeatability. Objectivity,
accuracy and repeatability can be achieved by analysing visual characteristics of the products using computer
vision. Particularly, automatic real time inspection systems based on texture analysis can be implemented using
Local Binary Pattern (LBP) techniques. A recent variation of the LBP techniques, named Geometric Local
Binary Pattern (GLBP) technique, showed an increase in the performance for detecting small changes of local
texture. In this paper a real time implementation of the algorithm is presented by using a Graphic Processing
Unit (GPU). The LBP and GLBP techniques are compared in terms of speed and accuracy while implemented
on a Central Processing Unit (CPU) and GPU environments. Algorithms are tested for detecting defects in
fabrics as well as for evaluating global deviations of texture, which are due to the degradation of the surface
in carpets. Results show that higher discriminant power between similar textures is obtained when using the
Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can
decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on
image analysis, have become an important alternative for replacing traditional inspections methods that involve
human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains,
offering more objective and reliable results for particular tasks than human inspection.
Costs of automated inspection systems development can be reduced using modular solutions with embedded
systems, in which an important advantage is the low energy consumption. Among the possibilities for developing
embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal
processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by
implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient
for real time applications.
In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix
operations, such that it is adequate for a real time application. The system was tested for defect detection
using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm
implementation, such that other algorithms may use of the ARM operations for fast implementations.
Small devices used in our day life are constructed with powerful architectures that can be used for industrial
applications when requiring portability and communication facilities. We present in this paper an example of
the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using
image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler
for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of
defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance
than using processors with float point arithmetic calculations.