Classification of surface defects on bridge cable based on PSO-SVM

Distributed machine vision system was applied for the detection on the cable surface defect of the cable-stayed bridge, and access to surface defects including longitudinal cracking, transverse cracking, surface erosion and scarring pit holes and other scars. In order to achieve the automatic classification of surface defects, firstly, part of the texture features, gray features and shape features on the defect image were selected as the target classification feature quantities; then the particle swarm optimization (PSO) was introduced to optimize the punitive coefficient and kernel function parameter of the support vector machine (SVM) model; and finally the objective of defects was identified with the help of the PSO-SVM classifier. Recognition experiments were performed on cable surface defects, presenting a recognition rate of 96.25 percent. The results showed that PSO-SVM has high recognition rate for classification of surface defects on bridge cable.