Ball planting defect identification technology based on machine vision
Ball planting defects are one of the common problems in the chip manufacturing process,which may lead to chip performance degradation or failure if not detected and repaired in time.Utilizing machine vision technology,designs and studies a technique for detecting chip seeding defects with the aim of enhancing quality control in the chip fabrication process.By assembling a comprehensive dataset of chip balling images,both normal and defective,and employing advanced computer vision algorithms for in-depth processing and analysis,the study undertakes a rigorous examination.During this process,image preprocessing techniques such as denoising and enhancement were utilized to improve the accuracy of defect detection.Further,image segmentation algorithms were applied to effectively isolate chip balling from the background,facilitating precise defect identification.Additionally,feature extraction algorithms were employed to identify the distinctive characteristics of chip balling,and classification algorithms were used to accurately differentiate between normal and defective chip samples.Not only does it improve the accuracy of chip defect detection,but it also provides important basis for quality control in industrial production,which has positive practical significance.
NETL
NSTL
万方数据
