A Multitarget Visual Attention Based Algorithm on Crack Detection of Industrial Explosives

Abstract

This paper is a novel study on crack detection of industrial explosives. The proposed algorithm consists of the following steps: (1) image preprocessing was performed according to the defect features of industrial explosives cartridge, and we developed an improved visual attention based algorithm. This proposed algorithm features a parametric analysis that can be implemented on the image according to the conspicuous maps with the introduction of the concept of defect discrimination ξ; (2) as compared with other algorithms, our method can realize real-time multitarget detection function; (3) a new analysis method, the IPV-WEN algorithm, was proposed to analyze the cartridge defects based on performance indices. Through comparison and experimentation, it was revealed that this method can achieve a detection accuracy of 97.9%, with detection time of 34.51 ms, which satisfied the requirement in the industrial explosives production.