APPLICATION OF IMAGE PROCESSING AND RESNET-50 MODEL IN DIAGNOSING DEFECTS IN MECHANICAL PRODUCT DETAILS

Abstract

Machine learning and computer vision play pivotal roles in detecting product defects across various industries, enhancing effectiveness, precision, and
minimizing labor expenditures. This journal utilizes image manipulation through the OpenCV, coupled with machine learning employing the ResNet-50 model,
to specifically identify surface defects and dimensions in bearings. Unlike prior research, the focus here lies on recognizing defects in mechanical parts demanding
precise machining. The ResNet-50 model showcased an impressive 98.5% accuracy in identifying faulty bearings. Notably, the recognition outcomes from this
model surpass the accuracy of other models like YOLO and SSD. This research demonstrates the effectiveness of integrating advanced image processing
techniques with machine learning models, particularly ResNet-50, in addressing the stringent requirements of identifying surface defects in mechanically critical
components. The successful application of this approach signifies its potential to revolutionize quality control processes, ensuring higher accuracy and reliability
in defect detection within industrial and manufacturing settings.