5373

5033

كارشناسي ارشد

هوش مصنوعي

اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر

1389

ده،105ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

رسول امير فتاحي، محمد علي منتظري

سعيد صدري

18/5/89

مهندسي برق و كامپيوتر

ID5033

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Detection and Classification of Grate Bar Defects in Palate Surface Using color Image Processing Narjes Sadat Chehrerazi N chehrerazi@ec iut ac ir Date of Submission 14 April 2010 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc language Farsi Supervisors Rasoul Amirfattahi fattahi@cc iut ac ir Mohammad Ali Montazeri montazer@cc iut ac ir Abstract Mobarakeh steel company is the biggest steel maker company in Iran Inspection of different units of this company e g pelletizing plant is done by human such that the quality is enhanced and the probabilistic costs are decreased In the peletizing unit pellets are posed on the pallets with 150 cm width and 360 cm length The pellets undergo drying and preheating in the stove There are four rows of grate bars on the surface of the pallet Each row consists of 90 grate bars Gradually the grate bars will be damaged because of the high temperature of stove sudden change of temperature and also because the pellets hit the grate bars The damages cause spaces between grate bars and this lead to losses in pelletizing unit If we observe and report the damages periodically we can prevent these losses through replacing the pallets if required For this reason pallets need permanent inspection Today this inspection is done by the human In this thesis a method for automatic detection of these damages is presented At first the pallet area is segmented from the image by three proposed algorithms and the pallet is divided to four areas such that each area has a row of grate bars Then in the image of one row of the grate bars the space between every two adjacent grate bars is segmented that we call them objects Therefore we use four methods segmentation according to statistical information of the color of images segmentation using k means algorithm segmentation using local k means algorithm and a proposed multistage segmentation algorithm Then 103 features color and shape feature are extracted from the objects Among these features 33 more effective features are selected using logistic regression Finally based on these selected features the objects are classified to intact and faulty classes thus the damages are detected with high accuracy Also the location of the damages is indicated on the pallet surface Key words Pallet grate bars segmentation feature extraction feature selection classification

رسول امير فتاحي، محمد علي منتظري

سعيد صدري