Research on Surface Flaw Detection of Industrial Components Based on Deep Learning Algorithm

Abstract

This paper primarily focuses on the research of industrial component surface defect detection algorithms using deep learning techniques. Taking the example of defect detection in the injector valve seat of automotive engine components, currently, the inspection of such small components heavily relies on manual visual inspection or traditional machine vision methods. Manual visual inspection is inefficient and cannot guarantee the speed and accuracy of detection. Traditional machine vision methods, while efficient, are sensitive to harsh environmental conditions and can be influenced by factors such as lighting, camera positioning, and background, resulting in poor robustness and limited feature extraction capabilities. To address these issues, this paper conducts research on a deep learning-based surface defect detection algorithm for injector valve seats.