IMAGE PROCESSING WITH DEEP LEARNING: SURFACE DEFECT DETECTION OF METAL GEARS THROUGH DEEP LEARNING

Abstract

Intelligent production requires improved data analytics and better technological possibilities to improve system performance and decision making. With the widespread use of the machine learning process, a growing need has arisen for processing extensive production data, equipped with high volumes, high speed, and high diversity. At this point, deep learning provides advanced analysis tools for processing and analyzing extensive production data. The deep convolutional neural network (DCNN) displays state-of-the-art performance on many grounds, including metal manufacturing surface defect detection. However, there is still space for improving the defect detection performance over generic DCNN models. The proposed approach performed better than the associated methods in the particular area of surface crack detection. The defect zones of disjointed results are classified into their unique classes by a DCNN. The experimental outcomes prove that this method meets the durability and efficiency requirements for metallic object defect detection. In time, it can also be extended to other detection methods. At the same time, the study will increase the accuracy quality of the features that can make a difference in the deep learning method for the detection of surface defects.