粒子群优化的深海图像暗边缘检测优化算法

为解决深海资源探测图像识别难题，提出一种基于粒子群优化的图像暗边缘检测优化算法。该算法通过指数型线性单元和高斯误差线性单元改进激活函数，根据Marr-Hildreth算子检测结果并结合改进激活函数构建暗边缘检测算法，利用粒子群对改进暗边缘检测算法进行训练和优化。最后，采用不同算法对水下11个数据集进行比较的结果表明：改进算法的峰值信噪比、结构相似度和边缘保持指数最高，分别达到18.769 6 dB、0.660 7和0.834 5；图像均方误差最低，为3 750.225 3；平均检测时间为0.667 4 s，比其他对比实验中性能最好的算法缩短了14%。

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

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.