粒子群优化的深海图像暗边缘检测优化算法

doi:10.3969/j.issn.0255-8297.2023.01.012

Abstract

Abstract: In order to solve the problem of image recognition for deep-sea resource detection, an optimization algorithm of image dark edge detection based on particle swarm optimization is proposed. The algorithm improves activation functions by using exponential linear unit and Gaussian error linear unit, constructs a dark edge detection algorithm in combination with the improved activation function according to the detection results of Marr-Hildreth operator, and uses particle swarm to train and optimize the improved dark edge detection algorithm. Finally, the proposed and several existing algorithms are applied and compared on 11 underwater data sets. Experimental results show that the proposed algorithm has the highest peak signal-to-noise ratio, structural similarity and edge retention index, reaching 18.769 6 dB, 0.660 7 and 0.834 5, respectively, and has the lowest mean square error of image of 3 750.225 3. Its average detection time is 0.667 4 s, about 14% shorter than that of the second best performance algorithm in the experiment.

Key words: deep-sea exploration, particle swarm optimization, Marr-Hildreth operator, dark edge detection

CLC Number:

TP391.41

ZOU Qianying, CHEN Huiyang, LI Yongsheng, HU Liwen, WANG Xiaofang. Optimization Algorithm for Dark Edge Detection of Deep-Sea Image Based on Particle Swarm Optimization[J]. Journal of Applied Sciences, 2023, 41(1): 153-169.

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Optimization Algorithm for Dark Edge Detection of Deep-Sea Image Based on Particle Swarm Optimization

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