A Defect Inspection for Explosive Cartridge Using an Improved Visual Attention and Image-Weighted Eigenvalue

Abstract

Surface defects of explosive cartridge in the automatic sorting process are of a small area, irregular shape, and random distribution, and all problematic characteristics that hinder surface defect detection. To address these issues, a new multidefect detection method has been proposed in this paper based on a combination of an improved visual attention model and image partitioning-weighted eigenvalue (IP-WEV). First, image preprocessing is carried out by a background estimation algorithm. Then, a new fusion operator based on defects discrimination is implemented in a visual attention model to integrate intensity, orientation, and edge conspicuity into a saliency graph, in which a saliency effect of defects is considered during the fusion process. Third, a saliency map is divided into image blocks based on the image variance. This allows for the extraction of image blocks including defects, the calculation of the weighted eigenvalue, and the determination of regions containing multidefect. The IP-WEV is used to make a decision for multidefect. The experimental results show this method's detection accuracy as 98.2%, with a less computation time and quickly detection speed. Therefore, this method could be adopted for on-line detection systems for explosive cartridge surface defects.