Multi-objective Optimization for Infrared Image Feature Detecting under Multi-performance Analysis

• Published in: 2019 American Control Conference (ACC) pp. 5071 - 5076
• Main Authors: Xue, Ting, Yin, Chun, Dadras, Sara, Huang, Xuegang, Cheng, Yuhua, Dadras, Soodeh
• Format: Conference Proceeding
• Language: English
• Published: American Automatic Control Council 01.07.2019
• Subjects: Feature detection; Feature extraction; Image sequences; Interference; Optimization methods; Photothermal effects
• DOI: 10.23919/ACC.2019.8814308

This paper develops a defect diagnosis algorithm with multi-objective optimization scheme in the thermal wave image technique, in order to automatically detect defects under M/OD impact damages. In the process of defect detection, there are large amount of data and large noise interference in the thermal image sequence got by infrared imager, and feature detection can effectively avoid these disadvantages. Obtaining the fusion defect image message by selecting representative temperature points of the infrared thermal response points after clustering is an effective method in many feature detection methods. The work of our paper is proposing a new algorithm based on multi-objective optimization method to detect feature for infrared thermal image sequence(ITIS), which carries out the multi-performance analysis to more accurately choose typical temperature points(TTPs). The method takes into account the diversity of different temperature points(TPs) and the likeness of the same TPs. Using the Tchebycheff decomposition method to decompose the built multi-objective problem, and algorithm can find pareto solutions of the problem by using the competition pressure caused by Tchebycheff aggregation. Besides, to verify the feasibility and effectiveness of our algorithm, we carry out related experiments and analysis, and confirm that the algorithm has good performance in feature detection for ITIS.