Ozturk, SabanAkdemir, Bayram2020-03-262020-03-262016978-1-5090-2188-8https://hdl.handle.net/20.500.12395/338363rd International Conference on Control, Decision and Information Technologies (CoDIT) -- APR 06-08, 2016 -- St Pauls Bay, MALTAIn this study, an effective segmentation method is presented for defect detection on the glass surface. Defect detection on the glass surface is compelling and strenuous job for human eyes. Transparency and reflection properties of the glass surface reduce success of image processing algorithms using detection of the factors that unwanted and affecting quality of products such as crack, scratch, bubble. Traditional methods have limited success and long processing time in this process. Therefore, fast and effective method has been proposed. In the proposed method (BiasFeed CNN), bias input which is single number value traditional CNN algorithm is converted bias template. Bias template is used to balance the brightness level of the image. Input image and bias template convolution is applied bias input. Through the contribution from bias input, background reflections and negative effects arising from transparency are decreased. The developed method is fast as traditional CNN, because it does not cause significant changes in the structure of traditional CNN. 35 pieces of glass was tested using the algorithm. Damages in the glass surface and location of these damages were determined. Accuracy rates of inspected images are; sensivity % 91, specificity % 99, accuracy % 98. BiasFeed CNN algorithm was tested on various images and it is more successful than traditional CNN algorithm.eninfo:eu-repo/semantics/closedAccesscellular neural networkbiasfeedglass defect detectiontexture inspectioncnnConference Object366371N/AWOS:000386533900066N/A