000			01655nam a22001817a 4500
082			_a006.4 _bU48A
100			_aUmbarje, Shweta _945425
245			_aAn Approach to Develop Artificially Intelligent Agent for Automatic Defect Detection for Smart Manufacturing _cby Shweta Umbarje
260			_aIIT Jodhpur _bDepartment of Computer Science and Technology _c2023
300			_avii,16p. _bHB
500			_aThe quality inspection of industrial products is becoming increasingly complex as the manufacturing industry advances in its comprehensive intelligent development. Steel surface defects contain a variety of intricate properties, and those brought on by various production lines typically differ from one another. As a result, the generalization performance of the detection algorithms for steel surface defects should be good. Currently, there is no available dataset with geometrical shapes containing steel defects. Therefore, this work introduces the creation of a new dataset, which is different from the NEU dataset in terms of the shape of the surface. We created a dataset of six different classes of geometrical shapes using the NEU dataset with the goal of identifying defects in the surface of steel. This research mainly concentrated on the concave-convex geometric shape for the creation of the dataset and introduced a new method to classify where the defects are located in those samples.
650			_aDepartment of Computer Science and Technology _945426
650			_aDefect Detection _945427
650			_aMTech Theses _945428
700			_aChattopadhyay, Chiranjoy _945429
700			_aDesai, Kaushalkumar A. _945430
942			_cTH
999			_c16585 _d16585