Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation.

ABSTRACT: Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, ena

SUBMITTER: Nie F 

PROVIDER: S-EPMC10984966 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature