Interactive Visual Inspection of a Rough-Alignment Plastic Part Based on HLAC Features and One-Class SVM

Abstract

Modern production lines for molded plastic parts often have automated inspection systems to detect defective parts reliably and efficiently. However, these conventional inspection systems have low flexibility and versatility, leading to difficulties when dealing with complicated requests such as when small quantities of many different parts are manufactured on the same production line. The proposed system can be implemented quickly using low-cost off-the-shelf components and does not require accurate alignment of production parts, reducing the need for manual inspections and increasing work efficiency when handling complex workloads. The inspection algorithm combines higher-order local auto correlation (HLAC) features with one-class support vector machine (one-class SVM) and principal component analysis (PCA) to extract, transform, and classify the differential feature vector between conforming and nonconforming plastic parts. To verify its validity and effectiveness, we compared defect detection accuracy and speed between the developed inspection system and manual inspection experimentally. Extremely high accuracy (Recall = 0.93, Specificity = 1.00) and speed (10 inspections in 30[sec]) was obtained with 7 types (1 conforming type, 6 nonconforming types) of sample parts (30 samples each). We demonstrated a 400 % increase in speed can be gained relative to manual inspection.

Keywords

• Higher-order local auto-correlation feature
• interactive
• one-class support vector machine
• plastic parts
• rough alignment
• visual inspection