Image Processing Techniques for Detecting Steel Defects with MATLAB and Machine Learning

This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using image processing and integrating it with computational intelligence in MATLAB. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machine learning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects’ recognition compared to other techniques. Thus, the’ findings of the apply demonstrate that the use of image processing and machine learning can offer a strong system for real time defect identification in steel production.