Wang Mengfei, Wang Weixing, Li Limin, Zhou Zhen
School of Information, Chang'an University, Xi'an 710064, China.
School of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China.
Entropy (Basel). 2022 Dec 6;24(12):1788. doi: 10.3390/e24121788.
Aggregate measurement and analysis are critical for civil engineering. Multiple entropy thresholding (MET) is inefficient, and the accuracy of related optimization strategies is unsatisfactory, which results in the segmented aggregate images lacking many surface roughness and aggregate edge features. Thus, this research proposes an autonomous segmentation model (i.e., PERSSA-MET) that optimizes MET based on the chaotic combination strategy sparrow search algorithm (SSA). First, aiming at the characteristics of the many extreme values of an aggregate image, a novel expansion parameter and range-control elite mutation strategies were studied and combined with piecewise mapping, named PERSSA, to improve the SSA's accuracy. This was compared with seven optimization algorithms using benchmark function experiments and a Wilcoxon rank-sum test, and the PERSSA's superiority was proved with the tests. Then, PERSSA was utilized to swiftly determine MET thresholds, and the METs were the Renyi entropy, symmetric cross entropy, and Kapur entropy. In the segmentation experiments of the aggregate images, it was proven that PERSSA-MET effectively segmented more details. Compared with SSA-MET, it achieved 28.90%, 12.55%, and 6.00% improvements in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), and the feature similarity (FSIM). Finally, a new parameter, overall merit weight proportion (OMWP), is suggested to calculate this segmentation method's superiority over all other algorithms. The results show that PERSSA-Renyi entropy outperforms well, and it can effectively keep the aggregate surface texture features and attain a balance between accuracy and speed.
集料测量与分析对土木工程至关重要。多熵阈值分割法（MET）效率低下，相关优化策略的准确性也不尽人意，导致分割后的集料图像缺少许多表面粗糙度和集料边缘特征。因此，本研究提出了一种基于混沌组合策略麻雀搜索算法（SSA）优化MET的自动分割模型（即PERSSA-MET）。首先，针对集料图像极值多的特点，研究了一种新颖的扩展参数和范围控制精英变异策略，并将其与分段映射相结合，命名为PERSSA，以提高SSA的精度。通过基准函数实验和威尔科克森秩和检验将其与七种优化算法进行比较，验证了PERSSA的优越性。然后，利用PERSSA快速确定MET阈值，这些MET分别为Renyi熵、对称交叉熵和Kapur熵。在集料图像分割实验中，证明了PERSSA-MET能有效分割更多细节。与SSA-MET相比，在峰值信噪比（PSNR）、结构相似性（SSIM）和特征相似性（FSIM）方面分别提高了28.90%、12.55%和6.00%。最后，提出了一个新参数——综合优点权重比例（OMWP），以计算该分割方法相对于所有其他算法的优越性。结果表明，PERSSA-Renyi熵表现出色，能够有效保留集料表面纹理特征，并在精度和速度之间取得平衡。
