Akademik Veri Yönetim Sistemi
2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Türkiye, 16 - 17 Eylül 2017
In industrial fabric productions, real time systems are needed to detect the fabric defects. This paper presents a real time defect detection approach which compares the time performances of Matlab and C++ programming languages. In the proposed method, important texture features of the fabric images are extracted using CoHOG method. Artificial neural network is used to classify the fabric defects. The developed method has been applied to detect the knitting fabric defects on a circular knitting machine. An overall defect detection success rate of 93% is achieved for the Matlab and C++ applications. To give an idea to the researches in defect detection area, real time operation speeds of Matlab and C++ codes have been examined. Especially, the number of images that can be processed in one second has been determined. While the Matlab based coding can process 3 images in 1 second, C++/Opencv based coding can process 55 images in 1 second. Previous works have rarely included the practical comparative evaluations of software environments. Therefore, we believe that the results of our industrial experiments will be a valuable resource for future works in this area.