This study focuses on adhered dirt to a product in production process, and also considers the relationship between dirt of inspection surface and defect detection in visual inspection utilizing peripheral vision. Specifically, images of inspection surface in an actual factory are analyzed using image analysis to do modeling. Moreover, dirt of inspection model, location and characteristics of defect are designed as experimental factors, and their effect on defect detection rate are evaluated. As a result, it is clarified that the defect detection rate becomes suddenly lower getting to the inspection surface dirtier. Consequently, defect that can be detected easily becomes harder to detect according as the inspection surface is dirtier.