Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization

Abstract

As an important part of automobile, the quality and safety of automobile engine high-pressure oil circuit seal parts are an important indicator of the manufacturer’s production process. In order to improve the detection accuracy and efficiency of seal parts in the traditional production process, the defect detection method on the surface of the seal was studied. A K-Means clustering image segmentation algorithm based on particle swarm optimization was proposed. To detect the surface defects of seals, first, preprocess the seal image. Then, use the SURF algorithm to extract the feature points of the seal image. Finally, according to the particle swarm fitness variance function, select the insertion point calculated by combining particle swarm optimization and K-Means algorithm. Through iteration, optimize the initial clustering center of K-Means algorithm. The efficiency of K-Means algorithm clustering iteration is improved. The test verifies the applicability of the algorithm in the actual process, and it can be used to accurately detect seals. Experimental results show that the detection accuracy rate reaches 98%, which is highly applicable to the actual production.