Work

Surface Defect Detection Method Using Saliency Linear Scanning Morphology for Silicon Steel Strip under Oil Pollution Interference

Year: 2014

Type: article

Abstract: Surface defect detection of silicon steel strip is an important section for non-destructive testing system in iron and steel industry. To detect the interesting defect objects for silicon steel strip under oil pollution interference, a new detection method based on saliency linear scanning morphology is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background. Meanwhile, a saliency map is obtained for the purpose of highlighting the potential objects. Then, the linear scanning operation is proposed to obtain the region of oil pollution. Finally, the morphology edge processing is proposed to remove the edge of oil pollution interference and the edge of reflective pseudo-defect. Experimental results demonstrate that the proposed method presents the good performance for detecting surface defects including wipe-crack-defect, scratch-defect and small-defect.

Source: ISIJ International

DOI: 10.2355/isijinternational.54.2598

Authors: Ke-Chen Song, Shao-Peng Hu, Yun-Hui Yan, Jun Li

Institutions: Northeastern University, Northeastern University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $700

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities